From 9e6555ba7e1e8c256165246330cee34f569758c7 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Thu, 24 Aug 2023 09:58:36 -0700 Subject: [PATCH 01/29] initial gait analysis progress --- Functions/gait_analysis.py | 52 +++++++ utils.py | 21 ++- utilsKinematics.py | 155 +++++++++++++++++++ utilsTRC.py | 298 +++++++++++++++++++++++++++++++++++++ 4 files changed, 523 insertions(+), 3 deletions(-) create mode 100644 Functions/gait_analysis.py create mode 100644 utilsTRC.py diff --git a/Functions/gait_analysis.py b/Functions/gait_analysis.py new file mode 100644 index 00000000..1878d587 --- /dev/null +++ b/Functions/gait_analysis.py @@ -0,0 +1,52 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Jul 5 13:23:12 2023 + +this function performs kinematic gait analysis + +@authors: Scott Uhlrich & Antoine Falisse +""" + +import os +import sys +sys.path.append("..") + +import utilsKinematics +# import utils + +def gait_analysis(trial_id,nGaitCycles=1): + + # session_id = utils.get_trial_json(trial_id)['session'] + # TODO DELETE + trialName = 'walk' + session_id = '03284efb-2244-4a48-aec9-abc34afdffc8' + + # Local data dir -> will be deleted with lambda instance + dataDir = os.path.abspath(os.path.join('../Data',session_id)) + os.makedirs(dataDir,exist_ok=True) + + # download data + # trialName = utils.download_trial(trial_id,dataDir,session_id=session_id) + + # init gait analysis + gait = utilsKinematics.gait_analysis(dataDir, trialName, + lowpass_cutoff_frequency_for_coordinate_values=-1, + n_gait_cycles=1) + + + scalar_names = {'gait_speed','stride_length'} + + gaitScalars = gait.get_scalars(scalar_names) + + # post results + + # TODO temp + return gaitScalars + + +# TODO delete. Testing as script +gaitScalars = gait_analysis('bf181007-d0f3-4395-8dc3-a0f0e5553761') + +test = 1 + + diff --git a/utils.py b/utils.py index faf720f3..70a44619 100644 --- a/utils.py +++ b/utils.py @@ -58,7 +58,7 @@ def get_created_at(trial): sessionJson['trials'].sort(key=get_created_at) return sessionJson - + # Returns a list of all sessions of the user. def get_user_sessions(): sessions = requests.get( @@ -245,6 +245,21 @@ def download_kinematics(session_id, folder=None, trialNames=None): return loadedTrialNames, modelName +# %% download pertinent trial data +def download_trial(trial_id, folder, session_id=None): + + trial = get_trial_json(trial_id) + if session_id is None: + session_id = trial['session_id'] + + # download model + get_model_and_metadata(session_id, folder) + + # download trc and mot + get_motion_data(trial_id,folder) + + return trial['name'] + # %% Storage file to numpy array. def storage_to_numpy(storage_file, excess_header_entries=0): """Returns the data from a storage file in a numpy format. Skips all lines @@ -359,7 +374,6 @@ def numpy_to_storage(labels, data, storage_file, datatype=None): f.close() - def download_videos_from_server(session_id,trial_id, isCalibration=False, isStaticPose=False, trial_name= None, session_path = None): @@ -595,4 +609,5 @@ def zipdir(path, ziph): # Write zip as a result to last trial for now if writeToDB: post_file_to_trial(session_zip,dynamic_ids[-1],tag='session_zip', - device_id='all') \ No newline at end of file + device_id='all') + diff --git a/utilsKinematics.py b/utilsKinematics.py index 82615443..3475891b 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -19,12 +19,16 @@ ''' import os +import glob import opensim import numpy as np import pandas as pd import scipy.interpolate as interpolate +from scipy.signal import find_peaks + from utilsProcessing import lowPassFilter +from utilsTRC import trc_2_dict class kinematics: @@ -371,3 +375,154 @@ def get_center_of_mass_accelerations(self, lowpass_cutoff_frequency=-1): com_accelerations = pd.DataFrame(data=data, columns=columns) return com_accelerations + +# %% +class gait_analysis: + + def __init__(self, dataDir, trialName, + lowpass_cutoff_frequency_for_coordinate_values=-1, + n_gait_cycles=1): + + self.lowpass_cutoff_frequency_for_coordinate_values = ( + lowpass_cutoff_frequency_for_coordinate_values) + + # Model. + opensim.Logger.setLevelString('error') + modelPath = glob.glob(os.path.join(dataDir, 'OpenSimData', 'Model', + '*.osim'))[0] + self.model = opensim.Model(modelPath) + self.model.initSystem() + + # Marker data + trcFilePath = os.path.join(dataDir,'MarkerData', + '{}.trc'.format(trialName)) + self.markerDict = trc_2_dict(trcFilePath) + + # Motion file with coordinate values. + motionPath = os.path.join(dataDir, 'OpenSimData', 'Kinematics', + '{}.mot'.format(trialName)) + + # Create time-series table with coordinate values. + self.table = opensim.TimeSeriesTable(motionPath) + tableProcessor = opensim.TableProcessor(self.table) + self.columnLabels = list(self.table.getColumnLabels()) + tableProcessor.append(opensim.TabOpUseAbsoluteStateNames()) + self.time = np.asarray(self.table.getIndependentColumn()) + + # Filter coordinate values. + if lowpass_cutoff_frequency_for_coordinate_values > 0: + tableProcessor.append( + opensim.TabOpLowPassFilter( + lowpass_cutoff_frequency_for_coordinate_values)) + + # Convert in radians. + self.table = tableProcessor.processAndConvertToRadians(self.model) + + # Trim if filtered. + if lowpass_cutoff_frequency_for_coordinate_values > 0: + time_temp = self.table.getIndependentColumn() + self.table.trim( + time_temp[self.table.getNearestRowIndexForTime(self.time[0])], + time_temp[self.table.getNearestRowIndexForTime(self.time[-1])]) + + # Set state trajectory + # self.stateTrajectory = opensim.StatesTrajectory.createFromStatesTable( + # self.model, self.table) + + # Coordinates. + self.coordinateSet = self.model.getCoordinateSet() + self.nCoordinates = self.coordinateSet.getSize() + self.coordinates = [self.coordinateSet.get(i).getName() + for i in range(self.nCoordinates)] + + # Segment by gait cycle + self.segment_walking(n_gait_cycles=n_gait_cycles) + + + def get_scalars(self,scalarNames): + + # verify that scalarNames are methods in gait_analysis + method_names = [func for func in dir(self) if callable(getattr(self, func))] + nonexistant_methods = [entry for entry in scalarNames if 'get_' + entry not in method_names] + + if len(nonexistant_methods) > 0: + raise Exception(str(['get_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') + + scalarDict = {} + for scalarName in scalarNames: + thisFunction = getattr(self, 'get_' + scalarName) + scalarDict[scalarName] = thisFunction() + + return scalarDict + + def get_stride_length(self): + # TODO dummy + stride_length = 3 + + return stride_length + + def get_gait_speed(self): + # TODO dummy + gait_speed = 2 + + return gait_speed + + def segment_walking(self, n_gait_cycles=1, filterFreq=6): + # subtract sacrum from foot + # visually, it looks like the position-based approach will be more robust + r_calc_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ + 'markers']['r.PSIS_study'])[:,0] + r_calc_rel_x = lowPassFilter(self.time, r_calc_rel_x, filterFreq) + r_calc_rel_dx = np.diff(r_calc_rel_x,append=r_calc_rel_x[-1]) + + r_toe_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ + 'markers']['r.PSIS_study'])[:,0] + r_toe_rel_x = lowPassFilter(self.time, r_toe_rel_x, filterFreq) + + + + + # position peak detection + # Find the peaks using find_peaks + peaks, _ = find_peaks(r_toe_rel_x) + + # Find the troughs using find_peaks with inverted data + troughs, _ = find_peaks(-r_toe_rel_x) + + import matplotlib.pyplot as plt + plt.plot(self.markerDict['time'],r_toe_rel_x) + plt.scatter(self.markerDict['time'][peaks], r_toe_rel_x[peaks], color='red', label='Peaks') + plt.scatter(self.markerDict['time'][troughs], r_toe_rel_x[troughs], color='blue', label='Troughs') + + print(self.markerDict['time'][peaks]) + print(self.markerDict['time'][troughs]) + + + return True + + + def get_coordinate_values(self, in_degrees=True, + lowpass_cutoff_frequency=-1): + + # Convert to degrees. + if in_degrees: + Qs = np.zeros((self.Qs.shape)) + Qs[:, self.idxColumnTrLabels] = self.Qs[:, self.idxColumnTrLabels] + Qs[:, self.idxColumnRotLabels] = ( + self.Qs[:, self.idxColumnRotLabels] * 180 / np.pi) + else: + Qs = self.Qs + + # Filter. + if lowpass_cutoff_frequency > 0: + Qs = lowPassFilter(self.time, Qs, lowpass_cutoff_frequency) + if self.lowpass_cutoff_frequency_for_coordinate_values > 0: + print("Warning: You are filtering the coordinate values a second time; coordinate values were filtered when creating your class object.") + + # Return as DataFrame. + data = np.concatenate( + (np.expand_dims(self.time, axis=1), Qs), axis=1) + columns = ['time'] + self.columnLabels + coordinate_values = pd.DataFrame(data=data, columns=columns) + + return coordinate_values \ No newline at end of file diff --git a/utilsTRC.py b/utilsTRC.py new file mode 100644 index 00000000..c0902956 --- /dev/null +++ b/utilsTRC.py @@ -0,0 +1,298 @@ +"""Manages the movement and use of data files.""" + +import os +import warnings +from scipy.spatial.transform import Rotation as R + +import numpy as np +from numpy.lib.recfunctions import append_fields + +class TRCFile(object): + """A plain-text file format for storing motion capture marker trajectories. + TRC stands for Track Row Column. + + The metadata for the file is stored in attributes of this object. + + See + http://simtk-confluence.stanford.edu:8080/display/OpenSim/Marker+(.trc)+Files + for more information. + + """ + def __init__(self, fpath=None, **kwargs): + #path=None, + #data_rate=None, + #camera_rate=None, + #num_frames=None, + #num_markers=None, + #units=None, + #orig_data_rate=None, + #orig_data_start_frame=None, + #orig_num_frames=None, + #marker_names=None, + #time=None, + #): + """ + Parameters + ---------- + fpath : str + Valid file path to a TRC (.trc) file. + + """ + self.marker_names = [] + if fpath != None: + self.read_from_file(fpath) + else: + for k, v in kwargs.items(): + setattr(self, k, v) + + def read_from_file(self, fpath): + # Read the header lines / metadata. + # --------------------------------- + # Split by any whitespace. + # TODO may cause issues with paths that have spaces in them. + f = open(fpath) + # These are lists of each entry on the first few lines. + first_line = f.readline().split() + # Skip the 2nd line. + f.readline() + third_line = f.readline().split() + fourth_line = f.readline().split() + f.close() + + # First line. + if len(first_line) > 3: + self.path = first_line[3] + else: + self.path = '' + + # Third line. + self.data_rate = float(third_line[0]) + self.camera_rate = float(third_line[1]) + self.num_frames = int(third_line[2]) + self.num_markers = int(third_line[3]) + self.units = third_line[4] + self.orig_data_rate = float(third_line[5]) + self.orig_data_start_frame = int(third_line[6]) + self.orig_num_frames = int(third_line[7]) + + # Marker names. + # The first and second column names are 'Frame#' and 'Time'. + self.marker_names = fourth_line[2:] + + len_marker_names = len(self.marker_names) + if len_marker_names != self.num_markers: + warnings.warn('Header entry NumMarkers, %i, does not ' + 'match actual number of markers, %i. Changing ' + 'NumMarkers to match actual number.' % ( + self.num_markers, len_marker_names)) + self.num_markers = len_marker_names + + # Load the actual data. + # --------------------- + col_names = ['frame_num', 'time'] + # This naming convention comes from OpenSim's Inverse Kinematics tool, + # when it writes model marker locations. + for mark in self.marker_names: + col_names += [mark + '_tx', mark + '_ty', mark + '_tz'] + dtype = {'names': col_names, + 'formats': ['int'] + ['float64'] * (3 * self.num_markers + 1)} + usecols = [i for i in range(3 * self.num_markers + 1 + 1)] + self.data = np.loadtxt(fpath, delimiter='\t', skiprows=5, dtype=dtype, + usecols=usecols) + self.time = self.data['time'] + + # Check the number of rows. + n_rows = self.time.shape[0] + if n_rows != self.num_frames: + warnings.warn('%s: Header entry NumFrames, %i, does not ' + 'match actual number of frames, %i, Changing ' + 'NumFrames to match actual number.' % (fpath, + self.num_frames, n_rows)) + self.num_frames = n_rows + + def __getitem__(self, key): + """See `marker()`. + + """ + return self.marker(key) + + def units(self): + return self.units + + def time(self): + this_dat = np.empty((self.num_frames, 1)) + this_dat[:, 0] = self.time + return this_dat + + def marker(self, name): + """The trajectory of marker `name`, given as a `self.num_frames` x 3 + array. The order of the columns is x, y, z. + + """ + this_dat = np.empty((self.num_frames, 3)) + this_dat[:, 0] = self.data[name + '_tx'] + this_dat[:, 1] = self.data[name + '_ty'] + this_dat[:, 2] = self.data[name + '_tz'] + return this_dat + + def add_marker(self, name, x, y, z): + """Add a marker, with name `name` to the TRCFile. + + Parameters + ---------- + name : str + Name of the marker; e.g., 'R.Hip'. + x, y, z: array_like + Coordinates of the marker trajectory. All 3 must have the same + length. + + """ + if (len(x) != self.num_frames or len(y) != self.num_frames or len(z) != + self.num_frames): + raise Exception('Length of data (%i, %i, %i) is not ' + 'NumFrames (%i).', len(x), len(y), len(z), self.num_frames) + self.marker_names += [name] + self.num_markers += 1 + if not hasattr(self, 'data'): + self.data = np.array(x, dtype=[('%s_tx' % name, 'float64')]) + self.data = append_fields(self.data, + ['%s_t%s' % (name, s) for s in 'yz'], + [y, z], usemask=False) + else: + self.data = append_fields(self.data, + ['%s_t%s' % (name, s) for s in 'xyz'], + [x, y, z], usemask=False) + + def marker_at(self, name, time): + x = np.interp(time, self.time, self.data[name + '_tx']) + y = np.interp(time, self.time, self.data[name + '_ty']) + z = np.interp(time, self.time, self.data[name + '_tz']) + return [x, y, z] + + def marker_exists(self, name): + """ + Returns + ------- + exists : bool + Is the marker in the TRCFile? + + """ + return name in self.marker_names + + def write(self, fpath): + """Write this TRCFile object to a TRC file. + + Parameters + ---------- + fpath : str + Valid file path to which this TRCFile is saved. + + """ + f = open(fpath, 'w') + + # Line 1. + f.write('PathFileType 4\t(X/Y/Z) %s\n' % os.path.split(fpath)[0]) + + # Line 2. + f.write('DataRate\tCameraRate\tNumFrames\tNumMarkers\t' + 'Units\tOrigDataRate\tOrigDataStartFrame\tOrigNumFrames\n') + + # Line 3. + f.write('%.1f\t%.1f\t%i\t%i\t%s\t%.1f\t%i\t%i\n' % ( + self.data_rate, self.camera_rate, self.num_frames, + self.num_markers, self.units, self.orig_data_rate, + self.orig_data_start_frame, self.orig_num_frames)) + + # Line 4. + f.write('Frame#\tTime\t') + for imark in range(self.num_markers): + f.write('%s\t\t\t' % self.marker_names[imark]) + f.write('\n') + + # Line 5. + f.write('\t\t') + for imark in np.arange(self.num_markers) + 1: + f.write('X%i\tY%s\tZ%s\t' % (imark, imark, imark)) + f.write('\n') + + # Line 6. + f.write('\n') + + # Data. + for iframe in range(self.num_frames): + f.write('%i' % (iframe + 1)) + f.write('\t%.7f' % self.time[iframe]) + for mark in self.marker_names: + idxs = [mark + '_tx', mark + '_ty', mark + '_tz'] + f.write('\t%.7f\t%.7f\t%.7f' % tuple( + self.data[coln][iframe] for coln in idxs)) + f.write('\n') + + f.close() + + def add_noise(self, noise_width): + """ add random noise to each component of the marker trajectory + The noise mean will be zero, with the noise_width being the + standard deviation. + + noise_width : int + """ + for imarker in range(self.num_markers): + components = ['_tx', '_ty', '_tz'] + for iComponent in range(3): + # generate noise + noise = np.random.normal(0, noise_width, self.num_frames) + # add noise to each component of marker data. + self.data[self.marker_names[imarker] + components[iComponent]] += noise + + def rotate(self, axis, value): + """ rotate the data. + + axis : rotation axis + value : angle in degree + """ + for imarker in range(self.num_markers): + + temp = np.zeros((self.num_frames, 3)) + temp[:,0] = self.data[self.marker_names[imarker] + '_tx'] + temp[:,1] = self.data[self.marker_names[imarker] + '_ty'] + temp[:,2] = self.data[self.marker_names[imarker] + '_tz'] + + r = R.from_euler(axis, value, degrees=True) + temp_rot = r.apply(temp) + + self.data[self.marker_names[imarker] + '_tx'] = temp_rot[:,0] + self.data[self.marker_names[imarker] + '_ty'] = temp_rot[:,1] + self.data[self.marker_names[imarker] + '_tz'] = temp_rot[:,2] + + def offset(self, axis, value): + """ offset the data. + + axis : rotation axis + value : offset in m + """ + for imarker in range(self.num_markers): + if axis.lower() == 'x': + self.data[self.marker_names[imarker] + '_tx'] += value + elif axis.lower() == 'y': + self.data[self.marker_names[imarker] + '_ty'] += value + elif axis.lower() == 'z': + self.data[self.marker_names[imarker] + '_tz'] += value + else: + raise ValueError("Axis not recognized") + +def trc_2_dict(pathFile, rotation=None): + # rotation is a dict, eg. {'y':90} with axis, angle for rotation + trc_dict = {} + trc_file = TRCFile(pathFile) + trc_dict['time'] = trc_file.time + trc_dict['marker_names'] = trc_file.marker_names + trc_dict['markers'] = {} + + if rotation != None: + for axis,angle in rotation.items(): + trc_file.rotate(axis,angle) + for count, marker in enumerate(trc_dict['marker_names']): + trc_dict['markers'][marker] = trc_file.marker(marker) + + return trc_dict From 08bbad22f02f4cbe0b8fd192b1fa0b42c98bd113 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Thu, 24 Aug 2023 16:48:19 -0700 Subject: [PATCH 02/29] segmentation and stride length --- Functions/gait_analysis.py | 6 +- utilsKinematics.py | 187 ++++++++++++++++++++++++++++++------- 2 files changed, 155 insertions(+), 38 deletions(-) diff --git a/Functions/gait_analysis.py b/Functions/gait_analysis.py index 1878d587..438bfe9f 100644 --- a/Functions/gait_analysis.py +++ b/Functions/gait_analysis.py @@ -12,7 +12,7 @@ sys.path.append("..") import utilsKinematics -# import utils +import utils def gait_analysis(trial_id,nGaitCycles=1): @@ -31,12 +31,12 @@ def gait_analysis(trial_id,nGaitCycles=1): # init gait analysis gait = utilsKinematics.gait_analysis(dataDir, trialName, lowpass_cutoff_frequency_for_coordinate_values=-1, - n_gait_cycles=1) + n_gait_cycles=2) scalar_names = {'gait_speed','stride_length'} - gaitScalars = gait.get_scalars(scalar_names) + gaitScalars = gait.calc_scalars(scalar_names) # post results diff --git a/utilsKinematics.py b/utilsKinematics.py index 3475891b..c82a85a3 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -435,71 +435,188 @@ def __init__(self, dataDir, trialName, self.coordinates = [self.coordinateSet.get(i).getName() for i in range(self.nCoordinates)] - # Segment by gait cycle - self.segment_walking(n_gait_cycles=n_gait_cycles) - + # Segment gait cycles + self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles) - def get_scalars(self,scalarNames): + # determine treadmill speed (0 if overground) + self.treadmillSpeed = self.calc_treadmill_speed() + + def calc_scalars(self,scalarNames): # verify that scalarNames are methods in gait_analysis method_names = [func for func in dir(self) if callable(getattr(self, func))] - nonexistant_methods = [entry for entry in scalarNames if 'get_' + entry not in method_names] + nonexistant_methods = [entry for entry in scalarNames if 'calc_' + entry not in method_names] if len(nonexistant_methods) > 0: - raise Exception(str(['get_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') + raise Exception(str(['calc_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') scalarDict = {} for scalarName in scalarNames: - thisFunction = getattr(self, 'get_' + scalarName) + thisFunction = getattr(self, 'calc_' + scalarName) scalarDict[scalarName] = thisFunction() return scalarDict - def get_stride_length(self): - # TODO dummy - stride_length = 3 + def calc_stride_length(self): + + # get calc positions based on self.gaitEvents['leg'] from self.markerDict + if self.gaitEvents['ipsilateralLeg'] == 'r': + leg = 'r' + else: + leg = 'L' + calc_position = self.markerDict['markers'][leg + '_calc_study'] + + # find stride length on treadmill + # difference in ipsilateral calcaneus position at heel strike + treadmill speed * time + strideLength = np.linalg.norm(calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ + calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + \ + self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) - return stride_length + # average across multiple strides + strideLength = np.mean(strideLength) + + return strideLength - def get_gait_speed(self): + def calc_gait_speed(self): # TODO dummy gait_speed = 2 return gait_speed - def segment_walking(self, n_gait_cycles=1, filterFreq=6): + def segment_walking(self, n_gait_cycles=1, filterFreq=6,leg='auto', + visualize=False): # subtract sacrum from foot # visually, it looks like the position-based approach will be more robust r_calc_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ 'markers']['r.PSIS_study'])[:,0] r_calc_rel_x = lowPassFilter(self.time, r_calc_rel_x, filterFreq) - r_calc_rel_dx = np.diff(r_calc_rel_x,append=r_calc_rel_x[-1]) - r_toe_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ + r_toe_rel_x = (self.markerDict['markers']['r_toe_study'] - self.markerDict[ 'markers']['r.PSIS_study'])[:,0] - r_toe_rel_x = lowPassFilter(self.time, r_toe_rel_x, filterFreq) - - + r_toe_rel_x = lowPassFilter(self.time, r_toe_rel_x, filterFreq) + + # repeat for left + l_calc_rel_x = (self.markerDict['markers']['L_calc_study'] - self.markerDict[ + 'markers']['L.PSIS_study'])[:,0] + l_calc_rel_x = lowPassFilter(self.time, l_calc_rel_x, filterFreq) + + l_toe_rel_x = (self.markerDict['markers']['L_toe_study'] - self.markerDict[ + 'markers']['L.PSIS_study'])[:,0] + l_toe_rel_x = lowPassFilter(self.time, l_toe_rel_x, filterFreq) + + # Find HS + rHS, _ = find_peaks(r_calc_rel_x) + lHS, _ = find_peaks(l_calc_rel_x) + + # Find TO + rTO, _ = find_peaks(-r_toe_rel_x) + lTO, _ = find_peaks(-l_toe_rel_x) + + if visualize==True: + import matplotlib.pyplot as plt + plt.close('all') + plt.figure(1) + plt.plot(self.markerDict['time'],r_toe_rel_x,label='toe') + plt.plot(self.markerDict['time'],r_calc_rel_x,label='calc') + plt.scatter(self.markerDict['time'][rHS], r_calc_rel_x[rHS], color='red', label='rHS') + plt.scatter(self.markerDict['time'][rTO], r_toe_rel_x[rTO], color='blue', label='rTO') + plt.legend() + + plt.figure(2) + plt.plot(self.markerDict['time'],l_toe_rel_x,label='toe') + plt.plot(self.markerDict['time'],l_calc_rel_x,label='calc') + plt.scatter(self.markerDict['time'][lHS], l_calc_rel_x[lHS], color='red', label='lHS') + plt.scatter(self.markerDict['time'][lTO], l_toe_rel_x[lTO], color='blue', label='lTO') + plt.legend() - # position peak detection - # Find the peaks using find_peaks - peaks, _ = find_peaks(r_toe_rel_x) - - # Find the troughs using find_peaks with inverted data - troughs, _ = find_peaks(-r_toe_rel_x) - - import matplotlib.pyplot as plt - plt.plot(self.markerDict['time'],r_toe_rel_x) - plt.scatter(self.markerDict['time'][peaks], r_toe_rel_x[peaks], color='red', label='Peaks') - plt.scatter(self.markerDict['time'][troughs], r_toe_rel_x[troughs], color='blue', label='Troughs') - - print(self.markerDict['time'][peaks]) - print(self.markerDict['time'][troughs]) - - - return True + # find the number of gait cycles for the foot of interest + if leg=='auto': + # find the last HS of either foot + if rHS[-1] > lHS[-1]: + leg = 'r' + else: + leg = 'l' + + # find the number of gait cycles for the foot of interest + if leg == 'r': + hsIps = rHS + toIps = rTO + hsCont = lHS + toCont = lTO + elif leg == 'l': + hsIps = lHS + toIps = lTO + hsCont = rHS + toCont = rTO + + n_gait_cycles = np.min([n_gait_cycles, len(hsIps)-1]) + gaitEvents_ips = np.zeros((n_gait_cycles, 3),dtype=np.int) + gaitEvents_cont = np.zeros((n_gait_cycles, 2),dtype=np.int) + if n_gait_cycles <1: + raise Exception('Not enough gait cycles found.') + + for i in range(n_gait_cycles): + # ipsilateral HS, TO, HS + gaitEvents_ips[i,0] = hsIps[-i-2] + gaitEvents_ips[i,1] = toIps[-i-1] + gaitEvents_ips[i,2] = hsIps[-i-1] + + # contralateral TO, HS + # iterate in reverse through contralateral HS and TO, finding the one that is within the range of gaitEvents_ips + hsContFound = False + toContFound = False + for j in range(len(toCont)): + if toCont[-j-1] > gaitEvents_ips[i,0] and toCont[-j-1] < gaitEvents_ips[i,2] and not toContFound: + gaitEvents_cont[i,0] = toCont[-j-1] + toContFound = True + + for j in range(len(hsCont)): + if hsCont[-j-1] > gaitEvents_ips[i,0] and hsCont[-j-1] < gaitEvents_ips[i,2] and not hsContFound: + gaitEvents_cont[i,1] = hsCont[-j-1] + hsContFound = True + + # making contralateral gait events optional + if not toContFound or not hsContFound: + raise Warning('Could not find contralateral gait event within ipsilateral gait event range.') + gaitEvents_cont[i,0] = np.nan + gaitEvents_cont[i,1] = np.nan + # convert gaitEvents to times using self.markerDict['time'] + gaitEventTimes_ips = self.markerDict['time'][gaitEvents_ips] + gaitEventTimes_cont = self.markerDict['time'][gaitEvents_cont] + + gaitEvents = {'ipsilateralIdx':gaitEvents_ips, + 'contralateralIdx':gaitEvents_cont, + 'ipsilateralTime':gaitEventTimes_ips, + 'contralateralTime':gaitEventTimes_cont, + 'eventNamesIpsilateral':['HS','TO','HS'], + 'eventNamesContralateral':['TO','HS'], + 'ipsilateralLeg':leg} + + return gaitEvents + + def calc_treadmill_speed(self): + if self.gaitEvents['ipsilateralLeg'] == 'r': + leg = 'r' + else: + leg = 'L' + toe_position = self.markerDict['markers'][leg + '_toe_study'] + + stanceLength = np.round(np.diff(self.gaitEvents['ipsilateralIdx'][:,:2])) + stanceTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) + startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.3*stanceLength).astype(int) + endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceLength).astype(int) + + toeDistanceStance = np.linalg.norm(toe_position[startIdx] - \ + toe_position[endIdx], axis=2) + + treadmillSpeed = np.mean(toeDistanceStance/stanceTime) + # overground + if treadmillSpeed < .2: + treadmillSpeed = 0 + + return treadmillSpeed def get_coordinate_values(self, in_degrees=True, lowpass_cutoff_frequency=-1): From d6eb90d61a24842f1d96e5b8acfa30a6f4eb3246 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Thu, 24 Aug 2023 23:51:10 -0700 Subject: [PATCH 03/29] gait speed, generic rotational coord id --- utilsKinematics.py | 90 ++++++++++++++++++++++++++-------------------- 1 file changed, 52 insertions(+), 38 deletions(-) diff --git a/utilsKinematics.py b/utilsKinematics.py index c82a85a3..df2ee211 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -124,15 +124,13 @@ def __init__(self, dataDir, trialName, self.coordinates = [self.coordinateSet.get(i).getName() for i in range(self.nCoordinates)] - # TODO: hard coded - # Translational coordinates. - columnTrLabels = [ - 'pelvis_tx', 'pelvis_ty', 'pelvis_tz'] + # Find rotational and translational coords self.idxColumnTrLabels = [ - self.columnLabels.index(i) for i in columnTrLabels] + self.columnLabels.index(i) for i in self.coordinates if \ + self.coordinateSet.get(i).getMotionType() == 2] self.idxColumnRotLabels = [ - self.columnLabels.index(i) for i in self.columnLabels - if not i in columnTrLabels] + self.columnLabels.index(i) for i in self.coordinates if \ + self.coordinateSet.get(i).getMotionType() == 1] # TODO: hard coded self.rootCoordinates = [ @@ -415,8 +413,8 @@ def __init__(self, dataDir, trialName, opensim.TabOpLowPassFilter( lowpass_cutoff_frequency_for_coordinate_values)) - # Convert in radians. - self.table = tableProcessor.processAndConvertToRadians(self.model) + # Process + self.table = tableProcessor.process(self.model) # Trim if filtered. if lowpass_cutoff_frequency_for_coordinate_values > 0: @@ -425,9 +423,7 @@ def __init__(self, dataDir, trialName, time_temp[self.table.getNearestRowIndexForTime(self.time[0])], time_temp[self.table.getNearestRowIndexForTime(self.time[-1])]) - # Set state trajectory - # self.stateTrajectory = opensim.StatesTrajectory.createFromStatesTable( - # self.model, self.table) + self.Qs = self.table.getMatrix().to_numpy() # Coordinates. self.coordinateSet = self.model.getCoordinateSet() @@ -435,6 +431,16 @@ def __init__(self, dataDir, trialName, self.coordinates = [self.coordinateSet.get(i).getName() for i in range(self.nCoordinates)] + # Find rotational and translational coords + self.idxColumnTrLabels = [ + self.columnLabels.index(i) for i in self.coordinates if \ + self.coordinateSet.get(i).getMotionType() == 2] + self.idxColumnRotLabels = [ + self.columnLabels.index(i) for i in self.coordinates if \ + self.coordinateSet.get(i).getMotionType() == 1] + + self.coordinateValues = self.get_coordinate_values() + # Segment gait cycles self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles) @@ -472,16 +478,46 @@ def calc_stride_length(self): calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + \ self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) - # average across multiple strides + # average across all strides strideLength = np.mean(strideLength) return strideLength def calc_gait_speed(self): - # TODO dummy - gait_speed = 2 + pelvis_position = np.vstack((self.coordinateValues['pelvis_tx'], + self.coordinateValues['pelvis_ty'], + self.coordinateValues['pelvis_tz'])).T + + gait_speed = (np.linalg.norm(pelvis_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ + pelvis_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2)) / \ + np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed + + # average across all strides + gait_speed = np.mean(gait_speed) return gait_speed + + def calc_treadmill_speed(self): + if self.gaitEvents['ipsilateralLeg'] == 'r': + leg = 'r' + else: + leg = 'L' + toe_position = self.markerDict['markers'][leg + '_toe_study'] + + stanceLength = np.round(np.diff(self.gaitEvents['ipsilateralIdx'][:,:2])) + stanceTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) + startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.3*stanceLength).astype(int) + endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceLength).astype(int) + + toeDistanceStance = np.linalg.norm(toe_position[startIdx] - \ + toe_position[endIdx], axis=2) + + treadmillSpeed = np.mean(toeDistanceStance/stanceTime) + # overground + if treadmillSpeed < .2: + treadmillSpeed = 0 + + return treadmillSpeed def segment_walking(self, n_gait_cycles=1, filterFreq=6,leg='auto', visualize=False): @@ -595,29 +631,7 @@ def segment_walking(self, n_gait_cycles=1, filterFreq=6,leg='auto', 'ipsilateralLeg':leg} return gaitEvents - - def calc_treadmill_speed(self): - if self.gaitEvents['ipsilateralLeg'] == 'r': - leg = 'r' - else: - leg = 'L' - toe_position = self.markerDict['markers'][leg + '_toe_study'] - - stanceLength = np.round(np.diff(self.gaitEvents['ipsilateralIdx'][:,:2])) - stanceTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.3*stanceLength).astype(int) - endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceLength).astype(int) - - toeDistanceStance = np.linalg.norm(toe_position[startIdx] - \ - toe_position[endIdx], axis=2) - - treadmillSpeed = np.mean(toeDistanceStance/stanceTime) - # overground - if treadmillSpeed < .2: - treadmillSpeed = 0 - - return treadmillSpeed - + def get_coordinate_values(self, in_degrees=True, lowpass_cutoff_frequency=-1): From 3154f72c295905a79e79a55b9d7bac651dc2cbe2 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 25 Aug 2023 00:22:16 -0700 Subject: [PATCH 04/29] start incorporating into walking dynamics example --- Examples/example_walking_opensimAD.py | 23 ++++++++++++++++++----- Functions/gait_analysis.py | 3 +-- utils.py | 10 ++++++++++ utilsKinematics.py | 4 ++++ 4 files changed, 33 insertions(+), 7 deletions(-) diff --git a/Examples/example_walking_opensimAD.py b/Examples/example_walking_opensimAD.py index 944acc48..166462fd 100644 --- a/Examples/example_walking_opensimAD.py +++ b/Examples/example_walking_opensimAD.py @@ -60,6 +60,8 @@ from utilsOpenSimAD import processInputsOpenSimAD, plotResultsOpenSimAD from mainOpenSimAD import run_tracking from utilsAuthentication import get_token +import utilsKinematics +import utils # %% OpenCap authentication. Visit https://app.opencap.ai/login to create an # account if you don't have one yet. @@ -76,6 +78,9 @@ # Insert the name of the trial you want to simulate. trial_name = 'walk_1_25ms' +# Insert the path to where you want the data to be downloaded. +dataFolder = os.path.join(baseDir, 'Data') + # Insert the type of activity you want to simulate. We have pre-defined settings # for different activities (more details above). Visit # ./UtilsDynamicSimulations/OpenSimAD/settingsOpenSimAD.py to see all available @@ -86,16 +91,24 @@ # Insert the time interval you want to simulate. It is recommended to simulate # trials shorter than 2s (more details above). Set to [] to simulate full trial. # We here selected a time window that corresponds to a full gait stride in order -# to use poeriodic constraints. -time_window = [5.7333333, 6.9333333] +# to use poriodic constraints +trial_id = utils.getTrialId(session_id,trial_name) +utils.download_trial(trial_id,os.path.join(dataFolder,session_id),session_id=session_id) +gait = utilsKinematics.gait_analysis(os.path.join(dataFolder,session_id), trial_name) +time_window = gait.gaitEvents['ipsilateralTimes'][0,0,2] + +# You can specify this time range as well +# time_window = [5.7333333, 6.9333333] # Insert the speed of the treadmill in m/s. A positive value indicates that the # subject is moving forward. You should ignore this parameter or set it to 0 if # the trial was not measured on a treadmill. -treadmill_speed = 1.25 +treadmill_speed = gait.treadmillSpeed + +# can also specify +# treadmill_speed = 1.25 -# Insert the path to where you want the data to be downloaded. -dataFolder = os.path.join(baseDir, 'Data') + # %% Sub-example 1: walking simulation with torque-driven model. # Insert a string to "name" you case. diff --git a/Functions/gait_analysis.py b/Functions/gait_analysis.py index 438bfe9f..f8a24503 100644 --- a/Functions/gait_analysis.py +++ b/Functions/gait_analysis.py @@ -22,8 +22,7 @@ def gait_analysis(trial_id,nGaitCycles=1): session_id = '03284efb-2244-4a48-aec9-abc34afdffc8' # Local data dir -> will be deleted with lambda instance - dataDir = os.path.abspath(os.path.join('../Data',session_id)) - os.makedirs(dataDir,exist_ok=True) + dataDir = os.path.abspath('../Data') # download data # trialName = utils.download_trial(trial_id,dataDir,session_id=session_id) diff --git a/utils.py b/utils.py index 70a44619..8ce97748 100644 --- a/utils.py +++ b/utils.py @@ -248,9 +248,12 @@ def download_kinematics(session_id, folder=None, trialNames=None): # %% download pertinent trial data def download_trial(trial_id, folder, session_id=None): + trial = get_trial_json(trial_id) if session_id is None: session_id = trial['session_id'] + + os.makedirs(folder,exist_ok=True) # download model get_model_and_metadata(session_id, folder) @@ -259,6 +262,13 @@ def download_trial(trial_id, folder, session_id=None): get_motion_data(trial_id,folder) return trial['name'] +# %% get trialID from name +def getTrialId(session_id,trial_name): + session = get_session_json(session_id) + + trial_id = [t['id'] for t in session['trials'] if t['name'] == trial_name] + + return trial_id[0] # %% Storage file to numpy array. def storage_to_numpy(storage_file, excess_header_entries=0): diff --git a/utilsKinematics.py b/utilsKinematics.py index df2ee211..ed2895e8 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -518,6 +518,10 @@ def calc_treadmill_speed(self): treadmillSpeed = 0 return treadmillSpeed + + def get_coordinates_normalized(self): + # TODO get normalized kinematic curves + test =1 def segment_walking(self, n_gait_cycles=1, filterFreq=6,leg='auto', visualize=False): From eb776633196b346c520275fa491d7e90bfeeb6d5 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 25 Aug 2023 00:35:54 -0700 Subject: [PATCH 05/29] clean gait times in kinetics example --- Examples/example_walking_opensimAD.py | 10 +++------- utilsProcessing.py | 14 +++++++++++++- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/Examples/example_walking_opensimAD.py b/Examples/example_walking_opensimAD.py index 166462fd..5e786a1f 100644 --- a/Examples/example_walking_opensimAD.py +++ b/Examples/example_walking_opensimAD.py @@ -60,8 +60,7 @@ from utilsOpenSimAD import processInputsOpenSimAD, plotResultsOpenSimAD from mainOpenSimAD import run_tracking from utilsAuthentication import get_token -import utilsKinematics -import utils +from utilsProcessing import segment_gait # %% OpenCap authentication. Visit https://app.opencap.ai/login to create an # account if you don't have one yet. @@ -92,10 +91,7 @@ # trials shorter than 2s (more details above). Set to [] to simulate full trial. # We here selected a time window that corresponds to a full gait stride in order # to use poriodic constraints -trial_id = utils.getTrialId(session_id,trial_name) -utils.download_trial(trial_id,os.path.join(dataFolder,session_id),session_id=session_id) -gait = utilsKinematics.gait_analysis(os.path.join(dataFolder,session_id), trial_name) -time_window = gait.gaitEvents['ipsilateralTimes'][0,0,2] +time_window, gaitObject = segment_gait(session_id, trial_name, dataFolder, gait_cycles_from_end=3) # You can specify this time range as well # time_window = [5.7333333, 6.9333333] @@ -103,7 +99,7 @@ # Insert the speed of the treadmill in m/s. A positive value indicates that the # subject is moving forward. You should ignore this parameter or set it to 0 if # the trial was not measured on a treadmill. -treadmill_speed = gait.treadmillSpeed +treadmill_speed = gaitObject.treadmillSpeed # can also specify # treadmill_speed = 1.25 diff --git a/utilsProcessing.py b/utilsProcessing.py index f2b6f0a2..995e9262 100644 --- a/utilsProcessing.py +++ b/utilsProcessing.py @@ -24,7 +24,7 @@ import numpy as np from scipy import signal import matplotlib.pyplot as plt -from utils import storage_to_dataframe +from utils import storage_to_dataframe, download_trial, getTrialId def lowPassFilter(time, data, lowpass_cutoff_frequency, order=4): @@ -35,6 +35,18 @@ def lowPassFilter(time, data, lowpass_cutoff_frequency, order=4): return dataFilt +# %% Segment gait +def segment_gait(session_id, trial_name, dataFolder, gait_cycles_from_end=0): + + from utilsKinematics import gait_analysis + + trial_id = getTrialId(session_id,trial_name) + download_trial(trial_id,os.path.join(dataFolder,session_id),session_id=session_id) + gait = gait_analysis(os.path.join(dataFolder,session_id), trial_name,n_gait_cycles=4) + heelstrikeTimes = gait.gaitEvents['ipsilateralTime'][gait_cycles_from_end,(0,2)].tolist() + + return heelstrikeTimes, gait + # %% Segment squats. def segmentSquats(ikFilePath, pelvis_ty=None, timeVec=None, visualize=False, filter_pelvis_ty=True, cutoff_frequency=4, height=.2): From feb1fe1dc7dea3c47e52704d756f918291782e52 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 25 Aug 2023 12:03:29 -0700 Subject: [PATCH 06/29] inherit kinematics class, add step width, compute gait frame --- Functions/gait_analysis.py | 10 +- utils.py | 1 - utilsKinematics.py | 196 ++++++++++++++++++------------------- 3 files changed, 100 insertions(+), 107 deletions(-) diff --git a/Functions/gait_analysis.py b/Functions/gait_analysis.py index f8a24503..bbf356bc 100644 --- a/Functions/gait_analysis.py +++ b/Functions/gait_analysis.py @@ -22,20 +22,20 @@ def gait_analysis(trial_id,nGaitCycles=1): session_id = '03284efb-2244-4a48-aec9-abc34afdffc8' # Local data dir -> will be deleted with lambda instance - dataDir = os.path.abspath('../Data') + sessionDir = os.path.join(os.path.abspath('../Data'),session_id) # download data - # trialName = utils.download_trial(trial_id,dataDir,session_id=session_id) + # trialName = utils.download_trial(trial_id,sessionDir,session_id=session_id) # init gait analysis - gait = utilsKinematics.gait_analysis(dataDir, trialName, + gait = utilsKinematics.gait_analysis(sessionDir, trialName, lowpass_cutoff_frequency_for_coordinate_values=-1, n_gait_cycles=2) - scalar_names = {'gait_speed','stride_length'} + scalar_names = {'gait_speed','stride_length','step_width'} - gaitScalars = gait.calc_scalars(scalar_names) + gaitScalars = gait.compute_scalars(scalar_names) # post results diff --git a/utils.py b/utils.py index 8ce97748..d92371cf 100644 --- a/utils.py +++ b/utils.py @@ -248,7 +248,6 @@ def download_kinematics(session_id, folder=None, trialNames=None): # %% download pertinent trial data def download_trial(trial_id, folder, session_id=None): - trial = get_trial_json(trial_id) if session_id is None: session_id = trial['session_id'] diff --git a/utilsKinematics.py b/utilsKinematics.py index ed2895e8..ab155625 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -32,7 +32,7 @@ class kinematics: - def __init__(self, dataDir, trialName, + def __init__(self, sessionDir, trialName, modelName='LaiUhlrich2022_scaled', lowpass_cutoff_frequency_for_coordinate_values=-1): @@ -41,13 +41,17 @@ def __init__(self, dataDir, trialName, # Model. opensim.Logger.setLevelString('error') - modelPath = os.path.join(dataDir, 'OpenSimData', 'Model', + # check if specified model name exists + modelPath = os.path.join(sessionDir, 'OpenSimData', 'Model', '{}.osim'.format(modelName)) + if not os.path.exists(modelPath): + modelPath = glob.glob(os.path.join(os.path.dirname(modelPath),'*.osim'))[0] + self.model = opensim.Model(modelPath) self.model.initSystem() # Motion file with coordinate values. - motionPath = os.path.join(dataDir, 'OpenSimData', 'Kinematics', + motionPath = os.path.join(sessionDir, 'OpenSimData', 'Kinematics', '{}.mot'.format(trialName)) # Create time-series table with coordinate values. @@ -375,95 +379,52 @@ def get_center_of_mass_accelerations(self, lowpass_cutoff_frequency=-1): return com_accelerations # %% -class gait_analysis: +class gait_analysis(kinematics): - def __init__(self, dataDir, trialName, + def __init__(self, sessionDir, trialName, lowpass_cutoff_frequency_for_coordinate_values=-1, n_gait_cycles=1): - - self.lowpass_cutoff_frequency_for_coordinate_values = ( - lowpass_cutoff_frequency_for_coordinate_values) - - # Model. - opensim.Logger.setLevelString('error') - modelPath = glob.glob(os.path.join(dataDir, 'OpenSimData', 'Model', - '*.osim'))[0] - self.model = opensim.Model(modelPath) - self.model.initSystem() + # inherit init from kinematics class + super().__init__(sessionDir, trialName, + lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) + # Marker data - trcFilePath = os.path.join(dataDir,'MarkerData', - '{}.trc'.format(trialName)) + trcFilePath = os.path.join(sessionDir,'MarkerData', + '{}.trc'.format(trialName)) self.markerDict = trc_2_dict(trcFilePath) - # Motion file with coordinate values. - motionPath = os.path.join(dataDir, 'OpenSimData', 'Kinematics', - '{}.mot'.format(trialName)) - - # Create time-series table with coordinate values. - self.table = opensim.TimeSeriesTable(motionPath) - tableProcessor = opensim.TableProcessor(self.table) - self.columnLabels = list(self.table.getColumnLabels()) - tableProcessor.append(opensim.TabOpUseAbsoluteStateNames()) - self.time = np.asarray(self.table.getIndependentColumn()) - - # Filter coordinate values. - if lowpass_cutoff_frequency_for_coordinate_values > 0: - tableProcessor.append( - opensim.TabOpLowPassFilter( - lowpass_cutoff_frequency_for_coordinate_values)) - - # Process - self.table = tableProcessor.process(self.model) - - # Trim if filtered. - if lowpass_cutoff_frequency_for_coordinate_values > 0: - time_temp = self.table.getIndependentColumn() - self.table.trim( - time_temp[self.table.getNearestRowIndexForTime(self.time[0])], - time_temp[self.table.getNearestRowIndexForTime(self.time[-1])]) - - self.Qs = self.table.getMatrix().to_numpy() - - # Coordinates. - self.coordinateSet = self.model.getCoordinateSet() - self.nCoordinates = self.coordinateSet.getSize() - self.coordinates = [self.coordinateSet.get(i).getName() - for i in range(self.nCoordinates)] - - # Find rotational and translational coords - self.idxColumnTrLabels = [ - self.columnLabels.index(i) for i in self.coordinates if \ - self.coordinateSet.get(i).getMotionType() == 2] - self.idxColumnRotLabels = [ - self.columnLabels.index(i) for i in self.coordinates if \ - self.coordinateSet.get(i).getMotionType() == 1] - + # Coordinate values self.coordinateValues = self.get_coordinate_values() # Segment gait cycles self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles) + self.nGaitCycles = np.shape(self.gaitEvents['ipsilateralIdx'])[0] # determine treadmill speed (0 if overground) - self.treadmillSpeed = self.calc_treadmill_speed() + self.treadmillSpeed = self.compute_treadmill_speed() + + # Compute COM trajectory and gait frame...used in multiple scalar computations + self.comValues = self.get_center_of_mass_values() + self.R_world_to_gait = self.compute_gait_frame() - def calc_scalars(self,scalarNames): + def compute_scalars(self,scalarNames): # verify that scalarNames are methods in gait_analysis method_names = [func for func in dir(self) if callable(getattr(self, func))] - nonexistant_methods = [entry for entry in scalarNames if 'calc_' + entry not in method_names] + nonexistant_methods = [entry for entry in scalarNames if 'compute_' + entry not in method_names] if len(nonexistant_methods) > 0: - raise Exception(str(['calc_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') + raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') scalarDict = {} for scalarName in scalarNames: - thisFunction = getattr(self, 'calc_' + scalarName) + thisFunction = getattr(self, 'compute_' + scalarName) scalarDict[scalarName] = thisFunction() return scalarDict - def calc_stride_length(self): + def compute_stride_length(self): # get calc positions based on self.gaitEvents['leg'] from self.markerDict if self.gaitEvents['ipsilateralLeg'] == 'r': @@ -483,13 +444,11 @@ def calc_stride_length(self): return strideLength - def calc_gait_speed(self): - pelvis_position = np.vstack((self.coordinateValues['pelvis_tx'], - self.coordinateValues['pelvis_ty'], - self.coordinateValues['pelvis_tz'])).T - - gait_speed = (np.linalg.norm(pelvis_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ - pelvis_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2)) / \ + def compute_gait_speed(self): + + comValuesArray = np.vstack((self.comValues['x'],self.comValues['y'],self.comValues['z'])).T + gait_speed = (np.linalg.norm(comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ + comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2)) / \ np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed # average across all strides @@ -497,7 +456,7 @@ def calc_gait_speed(self): return gait_speed - def calc_treadmill_speed(self): + def compute_treadmill_speed(self): if self.gaitEvents['ipsilateralLeg'] == 'r': leg = 'r' else: @@ -513,12 +472,73 @@ def calc_treadmill_speed(self): toe_position[endIdx], axis=2) treadmillSpeed = np.mean(toeDistanceStance/stanceTime) + # overground if treadmillSpeed < .2: treadmillSpeed = 0 return treadmillSpeed + def compute_step_width(self): + # get ankle joint center positions + if self.gaitEvents['ipsilateralLeg'] == 'r': + leg = 'r' + contLeg = 'L' + else: + leg = 'L' + contLeg = 'r' + ankle_position_ips = (self.markerDict['markers'][leg + '_ankle_study'] + + self.markerDict['markers'][leg + '_mankle_study'])/2 + ankle_position_cont = (self.markerDict['markers'][contLeg + '_ankle_study'] + + self.markerDict['markers'][contLeg + '_mankle_study'])/2 + + ankleVector = ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - \ + ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]] + + ankleVector_inGaitFrame = np.array([np.dot(ankleVector[i,:], self.R_world_to_gait[i,:,:]) \ + for i in range(self.nGaitCycles)]) + + # step width is z distance + stepWidth = np.abs(ankleVector_inGaitFrame[:,2]) + + # average across gait cycles + stepWidth = np.mean(stepWidth) + + return stepWidth + + def compute_gait_frame(self): + # Create frame for each gait cycle with x: pelvis heading, + # z: average vector between ASIS during gait cycle, y: cross. + + # Pelvis center trajectory + pelvisMarkerNames = ['r.ASIS_study','L.ASIS_study','r.PSIS_study','L.PSIS_study'] + pelvisMarkers = [self.markerDict['markers'][mkr] for mkr in pelvisMarkerNames] + pelvisCenter = np.mean(np.array(pelvisMarkers),axis=0) + + # vector from left ASIS to right ASIS + asisMarkerNames = ['L.ASIS_study','r.ASIS_study'] + asisMarkers = [self.markerDict['markers'][mkr] for mkr in asisMarkerNames] + asisVector = np.squeeze(np.diff(np.array(asisMarkers),axis=0)) + + # heading vector per gait cycle + x = np.diff(pelvisCenter[self.gaitEvents['ipsilateralIdx'][:,(0,2)],:],axis=1)[:,0,:] + x = x / np.linalg.norm(x,axis=1,keepdims=True) + + # mean asis vector over gait cycle + z = np.zeros((self.nGaitCycles,3)) + for i in range(self.nGaitCycles): + z[i,:] = np.mean(asisVector[self.gaitEvents['ipsilateralIdx'][i,0]: \ + self.gaitEvents['ipsilateralIdx'][i,2]],axis=0) + z = z / np.linalg.norm(z,axis=1,keepdims=True) + + # cross to get y + y = np.cross(z,x) + + # 3x3xnSteps + R_lab_to_gait = np.stack((x.T,y.T,z.T),axis=1).transpose((2, 0, 1)) + + return R_lab_to_gait + def get_coordinates_normalized(self): # TODO get normalized kinematic curves test =1 @@ -634,30 +654,4 @@ def segment_walking(self, n_gait_cycles=1, filterFreq=6,leg='auto', 'eventNamesContralateral':['TO','HS'], 'ipsilateralLeg':leg} - return gaitEvents - - def get_coordinate_values(self, in_degrees=True, - lowpass_cutoff_frequency=-1): - - # Convert to degrees. - if in_degrees: - Qs = np.zeros((self.Qs.shape)) - Qs[:, self.idxColumnTrLabels] = self.Qs[:, self.idxColumnTrLabels] - Qs[:, self.idxColumnRotLabels] = ( - self.Qs[:, self.idxColumnRotLabels] * 180 / np.pi) - else: - Qs = self.Qs - - # Filter. - if lowpass_cutoff_frequency > 0: - Qs = lowPassFilter(self.time, Qs, lowpass_cutoff_frequency) - if self.lowpass_cutoff_frequency_for_coordinate_values > 0: - print("Warning: You are filtering the coordinate values a second time; coordinate values were filtered when creating your class object.") - - # Return as DataFrame. - data = np.concatenate( - (np.expand_dims(self.time, axis=1), Qs), axis=1) - columns = ['time'] + self.columnLabels - coordinate_values = pd.DataFrame(data=data, columns=columns) - - return coordinate_values \ No newline at end of file + return gaitEvents \ No newline at end of file From df5454ec530b030bd7a8a7a67e6d9b0823506e1b Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 25 Aug 2023 15:07:00 -0700 Subject: [PATCH 07/29] get coordinates normalized, fix treadmill speed --- Examples/example_walking_opensimAD.py | 2 +- Functions/gait_analysis.py | 52 ++++++++----- utilsKinematics.py | 108 ++++++++++++++++++-------- 3 files changed, 110 insertions(+), 52 deletions(-) diff --git a/Examples/example_walking_opensimAD.py b/Examples/example_walking_opensimAD.py index 5e786a1f..5c3c8ccd 100644 --- a/Examples/example_walking_opensimAD.py +++ b/Examples/example_walking_opensimAD.py @@ -101,7 +101,7 @@ # the trial was not measured on a treadmill. treadmill_speed = gaitObject.treadmillSpeed -# can also specify +# can also specify manually # treadmill_speed = 1.25 diff --git a/Functions/gait_analysis.py b/Functions/gait_analysis.py index bbf356bc..7c261f2c 100644 --- a/Functions/gait_analysis.py +++ b/Functions/gait_analysis.py @@ -14,38 +14,52 @@ import utilsKinematics import utils -def gait_analysis(trial_id,nGaitCycles=1): +def gait_analysis(trial_id,scalar_names=None,n_gait_cycles=1,filterFreq=6): - # session_id = utils.get_trial_json(trial_id)['session'] - # TODO DELETE - trialName = 'walk' - session_id = '03284efb-2244-4a48-aec9-abc34afdffc8' + session_id = utils.get_trial_json(trial_id)['session'] # Local data dir -> will be deleted with lambda instance sessionDir = os.path.join(os.path.abspath('../Data'),session_id) # download data - # trialName = utils.download_trial(trial_id,sessionDir,session_id=session_id) + trialName = utils.download_trial(trial_id,sessionDir,session_id=session_id) # init gait analysis - gait = utilsKinematics.gait_analysis(sessionDir, trialName, - lowpass_cutoff_frequency_for_coordinate_values=-1, - n_gait_cycles=2) + gait_r = utilsKinematics.gait_analysis(sessionDir, trialName, leg='r', + lowpass_cutoff_frequency_for_coordinate_values=filterFreq, + n_gait_cycles=n_gait_cycles) + gait_l = utilsKinematics.gait_analysis(sessionDir, trialName, leg='l', + lowpass_cutoff_frequency_for_coordinate_values=filterFreq, + n_gait_cycles=n_gait_cycles) + + # compute scalars and get time-normalized kinematics + gaitResults = {} + gaitResults['scalars_r'] = gait_r.compute_scalars(scalar_names) + gaitResults['curves_r'] = gait_r.get_coordinates_normalized_time() + gaitResults['scalars_l'] = gait_l.compute_scalars(scalar_names) + gaitResults['curves_l'] = gait_l.get_coordinates_normalized_time() + # TODO post results to server - scalar_names = {'gait_speed','stride_length','step_width'} - - gaitScalars = gait.compute_scalars(scalar_names) - - # post results - - # TODO temp - return gaitScalars + # TODO delete return once posted to server + return gaitResults # TODO delete. Testing as script -gaitScalars = gait_analysis('bf181007-d0f3-4395-8dc3-a0f0e5553761') -test = 1 +# overground trial +trial_id = 'bf181007-d0f3-4395-8dc3-a0f0e5553761' + +# treadmill trial 1.25m/s +# session_id = "4d5c3eb1-1a59-4ea1-9178-d3634610561c" +# trial_name = 'walk_1_25ms' +# trial_id = utils.getTrialId(session_id,trial_name) + +scalar_names = {'gait_speed','stride_length','step_width'} + +gaitResults = gait_analysis(trial_id,scalar_names=scalar_names, + n_gait_cycles=4) + + diff --git a/utilsKinematics.py b/utilsKinematics.py index ab155625..c887f2b4 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -381,7 +381,7 @@ def get_center_of_mass_accelerations(self, lowpass_cutoff_frequency=-1): # %% class gait_analysis(kinematics): - def __init__(self, sessionDir, trialName, + def __init__(self, sessionDir, trialName, leg='auto', lowpass_cutoff_frequency_for_coordinate_values=-1, n_gait_cycles=1): @@ -389,16 +389,20 @@ def __init__(self, sessionDir, trialName, super().__init__(sessionDir, trialName, lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) - # Marker data + # Marker data load and filter trcFilePath = os.path.join(sessionDir,'MarkerData', '{}.trc'.format(trialName)) self.markerDict = trc_2_dict(trcFilePath) - + if lowpass_cutoff_frequency_for_coordinate_values > 0: + self.markerDict['markers'] = { + marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency_for_coordinate_values) \ + for marker_name, data in self.markerDict['markers'].items()} + # Coordinate values self.coordinateValues = self.get_coordinate_values() # Segment gait cycles - self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles) + self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles,leg=leg) self.nGaitCycles = np.shape(self.gaitEvents['ipsilateralIdx'])[0] # determine treadmill speed (0 if overground) @@ -409,9 +413,16 @@ def __init__(self, sessionDir, trialName, self.R_world_to_gait = self.compute_gait_frame() def compute_scalars(self,scalarNames): - + # verify that scalarNames are methods in gait_analysis method_names = [func for func in dir(self) if callable(getattr(self, func))] + possibleMethods = [entry for entry in method_names if 'compute_' in entry] + + if scalarNames is None: + print('No scalars defined, these methods are available:') + print(*possibleMethods) + return + nonexistant_methods = [entry for entry in scalarNames if 'compute_' + entry not in method_names] if len(nonexistant_methods) > 0: @@ -461,20 +472,22 @@ def compute_treadmill_speed(self): leg = 'r' else: leg = 'L' - toe_position = self.markerDict['markers'][leg + '_toe_study'] - - stanceLength = np.round(np.diff(self.gaitEvents['ipsilateralIdx'][:,:2])) - stanceTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.3*stanceLength).astype(int) - endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceLength).astype(int) + foot_position = self.markerDict['markers'][leg + '_ankle_study'] - toeDistanceStance = np.linalg.norm(toe_position[startIdx] - \ - toe_position[endIdx], axis=2) + stanceTimeLength = np.round(np.diff(self.gaitEvents['ipsilateralIdx'][:,:2])) + startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.1*stanceTimeLength).astype(int) + endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceTimeLength).astype(int) + + # average instantaneous velocities + dt = np.diff(self.markerDict['time'][:2])[0] + for i in range(self.nGaitCycles): + footVel = np.linalg.norm(np.mean(np.diff( + foot_position[startIdx[i,0]:endIdx[i,0],:],axis=0),axis=0)/dt) - treadmillSpeed = np.mean(toeDistanceStance/stanceTime) + treadmillSpeed = np.mean(footVel) # overground - if treadmillSpeed < .2: + if treadmillSpeed < .3: treadmillSpeed = 0 return treadmillSpeed @@ -510,21 +523,34 @@ def compute_gait_frame(self): # Create frame for each gait cycle with x: pelvis heading, # z: average vector between ASIS during gait cycle, y: cross. - # Pelvis center trajectory + # Pelvis center trajectory (for overground heading vector) pelvisMarkerNames = ['r.ASIS_study','L.ASIS_study','r.PSIS_study','L.PSIS_study'] pelvisMarkers = [self.markerDict['markers'][mkr] for mkr in pelvisMarkerNames] pelvisCenter = np.mean(np.array(pelvisMarkers),axis=0) - # vector from left ASIS to right ASIS + # ankle trajectory (for treadmill heading vector) + leg = self.gaitEvents['ipsilateralLeg'] + if leg == 'l': leg='L' + anklePos = self.markerDict['markers'][leg + '_ankle_study'] + + # vector from left ASIS to right ASIS (for mediolateral direction) asisMarkerNames = ['L.ASIS_study','r.ASIS_study'] asisMarkers = [self.markerDict['markers'][mkr] for mkr in asisMarkerNames] asisVector = np.squeeze(np.diff(np.array(asisMarkers),axis=0)) # heading vector per gait cycle - x = np.diff(pelvisCenter[self.gaitEvents['ipsilateralIdx'][:,(0,2)],:],axis=1)[:,0,:] - x = x / np.linalg.norm(x,axis=1,keepdims=True) + # if overground, use pelvis center trajectory; treadmill: ankle trajectory + if self.treadmillSpeed == 0: + x = np.diff(pelvisCenter[self.gaitEvents['ipsilateralIdx'][:,(0,2)],:],axis=1)[:,0,:] + x = x / np.linalg.norm(x,axis=1,keepdims=True) + else: + x = np.zeros((self.nGaitCycles,3)) + for i in range(self.nGaitCycles): + x[i,:] = anklePos[self.gaitEvents['ipsilateralIdx'][i,2]] - \ + anklePos[self.gaitEvents['ipsilateralIdx'][i,1]] + x = x / np.linalg.norm(x,axis=1,keepdims=True) - # mean asis vector over gait cycle + # mean ASIS vector over gait cycle z = np.zeros((self.nGaitCycles,3)) for i in range(self.nGaitCycles): z[i,:] = np.mean(asisVector[self.gaitEvents['ipsilateralIdx'][i,0]: \ @@ -539,30 +565,42 @@ def compute_gait_frame(self): return R_lab_to_gait - def get_coordinates_normalized(self): - # TODO get normalized kinematic curves - test =1 + def get_coordinates_normalized_time(self): + + colNames = self.coordinateValues.columns + data = self.coordinateValues.to_numpy(copy=True) + coordValuesNorm = [] + for i in range(self.nGaitCycles): + coordValues = data[self.gaitEvents['ipsilateralIdx'][i,0]:self.gaitEvents['ipsilateralIdx'][i,2]] + coordValuesNorm.append(np.stack([np.interp(np.linspace(0,100,101), + np.linspace(0,100,len(coordValues)),coordValues[:,i]) \ + for i in range(coordValues.shape[1])],axis=1)) + + coordinateValuesTimeNormalized = {} + # average and sd + coordVals_mean = np.mean(np.array(coordValuesNorm),axis=0) + coordinateValuesTimeNormalized['mean'] = pd.DataFrame(data=coordVals_mean, columns=colNames) + + #return to dataframe + coordinateValuesTimeNormalized['indiv'] = [pd.DataFrame(data=d, columns=colNames) for d in coordValuesNorm] + + + + return coordinateValuesTimeNormalized - def segment_walking(self, n_gait_cycles=1, filterFreq=6,leg='auto', - visualize=False): + def segment_walking(self, n_gait_cycles=1, leg='auto', visualize=False): # subtract sacrum from foot # visually, it looks like the position-based approach will be more robust r_calc_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ 'markers']['r.PSIS_study'])[:,0] - r_calc_rel_x = lowPassFilter(self.time, r_calc_rel_x, filterFreq) - r_toe_rel_x = (self.markerDict['markers']['r_toe_study'] - self.markerDict[ 'markers']['r.PSIS_study'])[:,0] - r_toe_rel_x = lowPassFilter(self.time, r_toe_rel_x, filterFreq) # repeat for left l_calc_rel_x = (self.markerDict['markers']['L_calc_study'] - self.markerDict[ 'markers']['L.PSIS_study'])[:,0] - l_calc_rel_x = lowPassFilter(self.time, l_calc_rel_x, filterFreq) - l_toe_rel_x = (self.markerDict['markers']['L_toe_study'] - self.markerDict[ 'markers']['L.PSIS_study'])[:,0] - l_toe_rel_x = lowPassFilter(self.time, l_toe_rel_x, filterFreq) # Find HS rHS, _ = find_peaks(r_calc_rel_x) @@ -619,8 +657,14 @@ def segment_walking(self, n_gait_cycles=1, filterFreq=6,leg='auto', for i in range(n_gait_cycles): # ipsilateral HS, TO, HS gaitEvents_ips[i,0] = hsIps[-i-2] - gaitEvents_ips[i,1] = toIps[-i-1] gaitEvents_ips[i,2] = hsIps[-i-1] + + # iterate in reverse through ipsilateral TO, finding the one that is within the range of gaitEvents_ips + toIpsFound = False + for j in range(len(toIps)): + if toIps[-j-1] > gaitEvents_ips[i,0] and toIps[-j-1] < gaitEvents_ips[i,2] and not toIpsFound: + gaitEvents_ips[i,1] = toIps[-j-1] + toIpsFound = True # contralateral TO, HS # iterate in reverse through contralateral HS and TO, finding the one that is within the range of gaitEvents_ips From ac33fe3d130e11108a70e5272b03786c1dbf1e0d Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 25 Aug 2023 15:14:34 -0700 Subject: [PATCH 08/29] tested kinetics --- Examples/example_walking_opensimAD.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/Examples/example_walking_opensimAD.py b/Examples/example_walking_opensimAD.py index 5c3c8ccd..1bdb22b6 100644 --- a/Examples/example_walking_opensimAD.py +++ b/Examples/example_walking_opensimAD.py @@ -91,18 +91,24 @@ # trials shorter than 2s (more details above). Set to [] to simulate full trial. # We here selected a time window that corresponds to a full gait stride in order # to use poriodic constraints -time_window, gaitObject = segment_gait(session_id, trial_name, dataFolder, gait_cycles_from_end=3) -# You can specify this time range as well -# time_window = [5.7333333, 6.9333333] +# automatically +# time_window, gaitObject = segment_gait(session_id, trial_name, dataFolder, gait_cycles_from_end=3) + +# manually +time_window = [5.7333333, 6.9333333] + + # Insert the speed of the treadmill in m/s. A positive value indicates that the # subject is moving forward. You should ignore this parameter or set it to 0 if # the trial was not measured on a treadmill. -treadmill_speed = gaitObject.treadmillSpeed -# can also specify manually -# treadmill_speed = 1.25 +# automatically +# treadmill_speed = gaitObject.treadmillSpeed + +#manually +treadmill_speed = 1.25 From 1052e28c26cadd520f13b5b5a5490dee212ed1e2 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 25 Aug 2023 15:32:00 -0700 Subject: [PATCH 09/29] cleaning --- Examples/example_walking_opensimAD.py | 4 ---- utilsKinematics.py | 4 +--- 2 files changed, 1 insertion(+), 7 deletions(-) diff --git a/Examples/example_walking_opensimAD.py b/Examples/example_walking_opensimAD.py index 1bdb22b6..6f19cc59 100644 --- a/Examples/example_walking_opensimAD.py +++ b/Examples/example_walking_opensimAD.py @@ -98,8 +98,6 @@ # manually time_window = [5.7333333, 6.9333333] - - # Insert the speed of the treadmill in m/s. A positive value indicates that the # subject is moving forward. You should ignore this parameter or set it to 0 if # the trial was not measured on a treadmill. @@ -110,8 +108,6 @@ #manually treadmill_speed = 1.25 - - # %% Sub-example 1: walking simulation with torque-driven model. # Insert a string to "name" you case. case = 'torque_driven' diff --git a/utilsKinematics.py b/utilsKinematics.py index c887f2b4..74d6fbc5 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -577,15 +577,13 @@ def get_coordinates_normalized_time(self): for i in range(coordValues.shape[1])],axis=1)) coordinateValuesTimeNormalized = {} - # average and sd + # average coordVals_mean = np.mean(np.array(coordValuesNorm),axis=0) coordinateValuesTimeNormalized['mean'] = pd.DataFrame(data=coordVals_mean, columns=colNames) #return to dataframe coordinateValuesTimeNormalized['indiv'] = [pd.DataFrame(data=d, columns=colNames) for d in coordValuesNorm] - - return coordinateValuesTimeNormalized def segment_walking(self, n_gait_cycles=1, leg='auto', visualize=False): From e94e44aebae72fcf9954c3be33a6cf6268f4f892 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Mon, 28 Aug 2023 08:38:25 -0700 Subject: [PATCH 10/29] reorganize files --- .../ActivityClasses/gaitAnalysis.py | 351 + .../MarkerData/walk.trc | 228 + .../OpenSimData/Kinematics/walk.mot | 233 + ...ied2017_poly_withArms_weldHand_scaled.osim | 14806 ++++++++++++++++ .../sessionMetadata.yaml | 20 + .../LambdaFunctions/analyzeGait.py | 13 +- utilsKinematics.py | 322 +- utilsProcessing.py | 5 +- 8 files changed, 15650 insertions(+), 328 deletions(-) create mode 100644 ActivityAnalyses/ActivityClasses/gaitAnalysis.py create mode 100644 ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/MarkerData/walk.trc create mode 100644 ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/OpenSimData/Kinematics/walk.mot create mode 100644 ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/OpenSimData/Model/LaiArnoldModified2017_poly_withArms_weldHand_scaled.osim create mode 100644 ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/sessionMetadata.yaml rename Functions/gait_analysis.py => ActivityAnalyses/LambdaFunctions/analyzeGait.py (82%) diff --git a/ActivityAnalyses/ActivityClasses/gaitAnalysis.py b/ActivityAnalyses/ActivityClasses/gaitAnalysis.py new file mode 100644 index 00000000..14192b83 --- /dev/null +++ b/ActivityAnalyses/ActivityClasses/gaitAnalysis.py @@ -0,0 +1,351 @@ +# -*- coding: utf-8 -*- +""" + --------------------------------------------------------------------------- + OpenCap processing: gaitAnalysis.py + --------------------------------------------------------------------------- + + Copyright 2023 Stanford University and the Authors + + Author(s): Antoine Falisse, Scott Uhlrich + + Licensed under the Apache License, Version 2.0 (the "License"); you may not + use this file except in compliance with the License. You may obtain a copy + of the License at http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import os +import sys +sys.path.append('../../') + +import numpy as np +import pandas as pd +from scipy.signal import find_peaks + +from utilsKinematics import kinematics +from utilsProcessing import lowPassFilter +from utilsTRC import trc_2_dict + + +class gait_analysis(kinematics): + + def __init__(self, sessionDir, trialName, leg='auto', + lowpass_cutoff_frequency_for_coordinate_values=-1, + n_gait_cycles=1): + + # inherit init from kinematics class + super().__init__(sessionDir, trialName, + lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) + + # Marker data load and filter + trcFilePath = os.path.join(sessionDir,'MarkerData', + '{}.trc'.format(trialName)) + self.markerDict = trc_2_dict(trcFilePath) + if lowpass_cutoff_frequency_for_coordinate_values > 0: + self.markerDict['markers'] = { + marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency_for_coordinate_values) \ + for marker_name, data in self.markerDict['markers'].items()} + + # Coordinate values + self.coordinateValues = self.get_coordinate_values() + + # Segment gait cycles + self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles,leg=leg) + self.nGaitCycles = np.shape(self.gaitEvents['ipsilateralIdx'])[0] + + # determine treadmill speed (0 if overground) + self.treadmillSpeed = self.compute_treadmill_speed() + + # Compute COM trajectory and gait frame...used in multiple scalar computations + self.comValues = self.get_center_of_mass_values() + self.R_world_to_gait = self.compute_gait_frame() + + def compute_scalars(self,scalarNames): + + # verify that scalarNames are methods in gait_analysis + method_names = [func for func in dir(self) if callable(getattr(self, func))] + possibleMethods = [entry for entry in method_names if 'compute_' in entry] + + if scalarNames is None: + print('No scalars defined, these methods are available:') + print(*possibleMethods) + return + + nonexistant_methods = [entry for entry in scalarNames if 'compute_' + entry not in method_names] + + if len(nonexistant_methods) > 0: + raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') + + scalarDict = {} + for scalarName in scalarNames: + thisFunction = getattr(self, 'compute_' + scalarName) + scalarDict[scalarName] = thisFunction() + + return scalarDict + + def compute_stride_length(self): + + # get calc positions based on self.gaitEvents['leg'] from self.markerDict + if self.gaitEvents['ipsilateralLeg'] == 'r': + leg = 'r' + else: + leg = 'L' + calc_position = self.markerDict['markers'][leg + '_calc_study'] + + # find stride length on treadmill + # difference in ipsilateral calcaneus position at heel strike + treadmill speed * time + strideLength = np.linalg.norm(calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ + calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + \ + self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + + # average across all strides + strideLength = np.mean(strideLength) + + return strideLength + + def compute_gait_speed(self): + + comValuesArray = np.vstack((self.comValues['x'],self.comValues['y'],self.comValues['z'])).T + gait_speed = (np.linalg.norm(comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ + comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2)) / \ + np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed + + # average across all strides + gait_speed = np.mean(gait_speed) + + return gait_speed + + def compute_treadmill_speed(self): + if self.gaitEvents['ipsilateralLeg'] == 'r': + leg = 'r' + else: + leg = 'L' + foot_position = self.markerDict['markers'][leg + '_ankle_study'] + + stanceTimeLength = np.round(np.diff(self.gaitEvents['ipsilateralIdx'][:,:2])) + startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.1*stanceTimeLength).astype(int) + endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceTimeLength).astype(int) + + # average instantaneous velocities + dt = np.diff(self.markerDict['time'][:2])[0] + for i in range(self.nGaitCycles): + footVel = np.linalg.norm(np.mean(np.diff( + foot_position[startIdx[i,0]:endIdx[i,0],:],axis=0),axis=0)/dt) + + treadmillSpeed = np.mean(footVel) + + # overground + if treadmillSpeed < .3: + treadmillSpeed = 0 + + return treadmillSpeed + + def compute_step_width(self): + # get ankle joint center positions + if self.gaitEvents['ipsilateralLeg'] == 'r': + leg = 'r' + contLeg = 'L' + else: + leg = 'L' + contLeg = 'r' + ankle_position_ips = (self.markerDict['markers'][leg + '_ankle_study'] + + self.markerDict['markers'][leg + '_mankle_study'])/2 + ankle_position_cont = (self.markerDict['markers'][contLeg + '_ankle_study'] + + self.markerDict['markers'][contLeg + '_mankle_study'])/2 + + ankleVector = ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - \ + ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]] + + ankleVector_inGaitFrame = np.array([np.dot(ankleVector[i,:], self.R_world_to_gait[i,:,:]) \ + for i in range(self.nGaitCycles)]) + + # step width is z distance + stepWidth = np.abs(ankleVector_inGaitFrame[:,2]) + + # average across gait cycles + stepWidth = np.mean(stepWidth) + + return stepWidth + + def compute_gait_frame(self): + # Create frame for each gait cycle with x: pelvis heading, + # z: average vector between ASIS during gait cycle, y: cross. + + # Pelvis center trajectory (for overground heading vector) + pelvisMarkerNames = ['r.ASIS_study','L.ASIS_study','r.PSIS_study','L.PSIS_study'] + pelvisMarkers = [self.markerDict['markers'][mkr] for mkr in pelvisMarkerNames] + pelvisCenter = np.mean(np.array(pelvisMarkers),axis=0) + + # ankle trajectory (for treadmill heading vector) + leg = self.gaitEvents['ipsilateralLeg'] + if leg == 'l': leg='L' + anklePos = self.markerDict['markers'][leg + '_ankle_study'] + + # vector from left ASIS to right ASIS (for mediolateral direction) + asisMarkerNames = ['L.ASIS_study','r.ASIS_study'] + asisMarkers = [self.markerDict['markers'][mkr] for mkr in asisMarkerNames] + asisVector = np.squeeze(np.diff(np.array(asisMarkers),axis=0)) + + # heading vector per gait cycle + # if overground, use pelvis center trajectory; treadmill: ankle trajectory + if self.treadmillSpeed == 0: + x = np.diff(pelvisCenter[self.gaitEvents['ipsilateralIdx'][:,(0,2)],:],axis=1)[:,0,:] + x = x / np.linalg.norm(x,axis=1,keepdims=True) + else: + x = np.zeros((self.nGaitCycles,3)) + for i in range(self.nGaitCycles): + x[i,:] = anklePos[self.gaitEvents['ipsilateralIdx'][i,2]] - \ + anklePos[self.gaitEvents['ipsilateralIdx'][i,1]] + x = x / np.linalg.norm(x,axis=1,keepdims=True) + + # mean ASIS vector over gait cycle + z = np.zeros((self.nGaitCycles,3)) + for i in range(self.nGaitCycles): + z[i,:] = np.mean(asisVector[self.gaitEvents['ipsilateralIdx'][i,0]: \ + self.gaitEvents['ipsilateralIdx'][i,2]],axis=0) + z = z / np.linalg.norm(z,axis=1,keepdims=True) + + # cross to get y + y = np.cross(z,x) + + # 3x3xnSteps + R_lab_to_gait = np.stack((x.T,y.T,z.T),axis=1).transpose((2, 0, 1)) + + return R_lab_to_gait + + def get_coordinates_normalized_time(self): + + colNames = self.coordinateValues.columns + data = self.coordinateValues.to_numpy(copy=True) + coordValuesNorm = [] + for i in range(self.nGaitCycles): + coordValues = data[self.gaitEvents['ipsilateralIdx'][i,0]:self.gaitEvents['ipsilateralIdx'][i,2]] + coordValuesNorm.append(np.stack([np.interp(np.linspace(0,100,101), + np.linspace(0,100,len(coordValues)),coordValues[:,i]) \ + for i in range(coordValues.shape[1])],axis=1)) + + coordinateValuesTimeNormalized = {} + # average + coordVals_mean = np.mean(np.array(coordValuesNorm),axis=0) + coordinateValuesTimeNormalized['mean'] = pd.DataFrame(data=coordVals_mean, columns=colNames) + + #return to dataframe + coordinateValuesTimeNormalized['indiv'] = [pd.DataFrame(data=d, columns=colNames) for d in coordValuesNorm] + + return coordinateValuesTimeNormalized + + def segment_walking(self, n_gait_cycles=1, leg='auto', visualize=False): + # subtract sacrum from foot + # visually, it looks like the position-based approach will be more robust + r_calc_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ + 'markers']['r.PSIS_study'])[:,0] + r_toe_rel_x = (self.markerDict['markers']['r_toe_study'] - self.markerDict[ + 'markers']['r.PSIS_study'])[:,0] + + # repeat for left + l_calc_rel_x = (self.markerDict['markers']['L_calc_study'] - self.markerDict[ + 'markers']['L.PSIS_study'])[:,0] + l_toe_rel_x = (self.markerDict['markers']['L_toe_study'] - self.markerDict[ + 'markers']['L.PSIS_study'])[:,0] + + # Find HS + rHS, _ = find_peaks(r_calc_rel_x) + lHS, _ = find_peaks(l_calc_rel_x) + + # Find TO + rTO, _ = find_peaks(-r_toe_rel_x) + lTO, _ = find_peaks(-l_toe_rel_x) + + if visualize==True: + import matplotlib.pyplot as plt + plt.close('all') + plt.figure(1) + plt.plot(self.markerDict['time'],r_toe_rel_x,label='toe') + plt.plot(self.markerDict['time'],r_calc_rel_x,label='calc') + plt.scatter(self.markerDict['time'][rHS], r_calc_rel_x[rHS], color='red', label='rHS') + plt.scatter(self.markerDict['time'][rTO], r_toe_rel_x[rTO], color='blue', label='rTO') + plt.legend() + + plt.figure(2) + plt.plot(self.markerDict['time'],l_toe_rel_x,label='toe') + plt.plot(self.markerDict['time'],l_calc_rel_x,label='calc') + plt.scatter(self.markerDict['time'][lHS], l_calc_rel_x[lHS], color='red', label='lHS') + plt.scatter(self.markerDict['time'][lTO], l_toe_rel_x[lTO], color='blue', label='lTO') + plt.legend() + + # find the number of gait cycles for the foot of interest + if leg=='auto': + # find the last HS of either foot + if rHS[-1] > lHS[-1]: + leg = 'r' + else: + leg = 'l' + + # find the number of gait cycles for the foot of interest + if leg == 'r': + hsIps = rHS + toIps = rTO + hsCont = lHS + toCont = lTO + elif leg == 'l': + hsIps = lHS + toIps = lTO + hsCont = rHS + toCont = rTO + + n_gait_cycles = np.min([n_gait_cycles, len(hsIps)-1]) + gaitEvents_ips = np.zeros((n_gait_cycles, 3),dtype=np.int) + gaitEvents_cont = np.zeros((n_gait_cycles, 2),dtype=np.int) + if n_gait_cycles <1: + raise Exception('Not enough gait cycles found.') + + for i in range(n_gait_cycles): + # ipsilateral HS, TO, HS + gaitEvents_ips[i,0] = hsIps[-i-2] + gaitEvents_ips[i,2] = hsIps[-i-1] + + # iterate in reverse through ipsilateral TO, finding the one that is within the range of gaitEvents_ips + toIpsFound = False + for j in range(len(toIps)): + if toIps[-j-1] > gaitEvents_ips[i,0] and toIps[-j-1] < gaitEvents_ips[i,2] and not toIpsFound: + gaitEvents_ips[i,1] = toIps[-j-1] + toIpsFound = True + + # contralateral TO, HS + # iterate in reverse through contralateral HS and TO, finding the one that is within the range of gaitEvents_ips + hsContFound = False + toContFound = False + for j in range(len(toCont)): + if toCont[-j-1] > gaitEvents_ips[i,0] and toCont[-j-1] < gaitEvents_ips[i,2] and not toContFound: + gaitEvents_cont[i,0] = toCont[-j-1] + toContFound = True + + for j in range(len(hsCont)): + if hsCont[-j-1] > gaitEvents_ips[i,0] and hsCont[-j-1] < gaitEvents_ips[i,2] and not hsContFound: + gaitEvents_cont[i,1] = hsCont[-j-1] + hsContFound = True + + # making contralateral gait events optional + if not toContFound or not hsContFound: + raise Warning('Could not find contralateral gait event within ipsilateral gait event range.') + gaitEvents_cont[i,0] = np.nan + gaitEvents_cont[i,1] = 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psoas_r recfem_r sart_r tfl_r + + + addlong_r addmagDist_r addmagIsch_r addmagMid_r addmagProx_r bflh_r glmax1_r glmax2_r glmax3_r glmed1_r glmed2_r glmed3_r glmin3_r semimem_r semiten_r + + + glmed1_r glmed2_r glmed3_r glmin1_r iliacus_r psoas_r tfl_r + + + glmin3_r piri_r + + + bflh_r bfsh_r gaslat_r gasmed_r grac_r sart_r semimem_r semiten_r + + + recfem_r vasint_r vaslat_r vasmed_r + + + edl_r ehl_r tibant_r + + + fdl_r fhl_r gaslat_r gasmed_r perbrev_r perlong_r soleus_r tibpost_r + + + edl_r perbrev_r perlong_r + + + ehl_r fdl_r fhl_r tibant_r tibpost_r + + + addbrev_l addlong_l addmagDist_l addmagIsch_l addmagMid_l addmagProx_l bflh_l grac_l semimem_l semiten_l + + + glmax1_l glmed1_l glmed2_l glmed3_l glmin1_l glmin2_l glmin3_l piri_l sart_l tfl_l + + + addbrev_l addlong_l glmin1_l grac_l iliacus_l psoas_l recfem_l sart_l tfl_l + + + addlong_l addmagDist_l addmagIsch_l addmagMid_l addmagProx_l bflh_l glmax1_l glmax2_l glmax3_l glmed1_l glmed2_l glmed3_l glmin3_l semimem_l semiten_l + + + glmin3_l piri_l + + + glmed1_l glmed2_l glmed3_l glmin1_l iliacus_l psoas_l tfl_l + + + bflh_l bfsh_l gaslat_l gasmed_l grac_l sart_l semimem_l semiten_l + + + recfem_l vasint_l vaslat_l vasmed_l + + + edl_l ehl_l tibant_l + + + fdl_l fhl_l gaslat_l gasmed_l perbrev_l perlong_l soleus_l tibpost_l + + + edl_l perbrev_l perlong_l + + + ehl_l fdl_l fhl_l tibant_l tibpost_l + + + + + + + + + /bodyset/pelvis + + 0.035013801362223873 -0.01402325932216808 0.17077441110785524 + + false + + + + /bodyset/pelvis + + 0.038893368506932952 -0.019680481906514391 -0.17884437842226619 + + false + + + + /bodyset/pelvis + + -0.20329734440781114 0.032129631012162596 0.06811423778667669 + + false + + + + /bodyset/pelvis + + -0.20449595453147956 0.028864899055276227 -0.068385434870986056 + + false + + + + /bodyset/femur_r + + -0.0041725507669734263 -0.40428351679683094 0.080741976747505828 + + false + + + + /bodyset/femur_r + + 0.031255262104863762 -0.39165864291872954 -0.042994040420724244 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-0.0075202131545348272 + + false + + + + /bodyset/calcn_l + + -0.04730876610607089 0.029887595849175531 0.01813073613483096 + + false + + + + /bodyset/calcn_l + + 0.19331409610901354 0.022281467769636888 -0.065726223512443421 + + false + + + + /bodyset/scapulaPhantom_r + + -0.0044826464207219779 -0.0049370221760858257 -0.030145617068376318 + + false + + + + /bodyset/scapulaPhantom_l + + 0.0069017982828444985 -0.0046485189027292151 0.035864668877402667 + + false + + + + /bodyset/torso + + -0.088113252657194482 0.44314863805658589 0.005795119062456236 + + false + + + + /bodyset/humerus_r + + 0.018274454276137116 -0.34112314648789288 0.048731878069698975 + + true + + + + /bodyset/humerus_r + + 0.0027411681414205671 -0.34843292819834765 -0.060914847587123726 + + true + + + + /bodyset/radius_r + + 0.0005749577912757182 -0.25873100607407323 0.057495779127571825 + + true + + + + /bodyset/radius_r + + -0.025298142816131599 -0.25873100607407323 -0.025298142816131599 + + true + + + + /bodyset/humerus_l + + 0.018274454276137116 -0.34112314648789288 -0.048731878069698975 + + true + + + + /bodyset/humerus_l + + 0.0027411681414205671 -0.34843292819834765 0.060914847587123726 + + true + + + + /bodyset/radius_l + + 0.0005749577912757182 -0.25873100607407323 -0.057495779127571825 + + true + + + + /bodyset/radius_l + + -0.025298142816131599 -0.25873100607407323 0.025298142816131599 + + true + + + + /bodyset/femur_r + + 0.078989158645530688 -0.14179171957657599 0.10642109288640017 + + false + + + + /bodyset/femur_r + + 0.038553617810355656 -0.24708472268296011 0.11788937480397815 + + false + + + + /bodyset/femur_r + + -0.052473484681342886 -0.14733234641534365 0.11660213111347617 + + false + + + + /bodyset/femur_l + + 0.067318182874585672 -0.14427829636933742 -0.11608524469299081 + + false + + + + /bodyset/femur_l + + 0.02593244885624417 -0.24864311131018102 -0.12292469069996614 + + false + + + + /bodyset/femur_l + + -0.055797192636025408 -0.14263682486314633 -0.10310091533250659 + + false + + + + /bodyset/tibia_r + + -0.014031910830953231 -0.12682329813104309 0.089436226980696587 + + false + + + + /bodyset/tibia_r + + 0.0098622033617341565 -0.25941000502930867 0.10274271497618748 + + false + + + + /bodyset/tibia_r + + -0.075056904426213239 -0.25621971668415933 0.08378677992678768 + + false + + + + /bodyset/tibia_l + + -0.027402758553175227 -0.1291731583781297 -0.083372535323507746 + + false + + + + /bodyset/tibia_l + + -0.001592782345544802 -0.26147495314914454 -0.095808901223968623 + + false + + + + /bodyset/tibia_l + + -0.083346230378508843 -0.26093371010649946 -0.074128738776999714 + + false + + + + /bodyset/pelvis + + -0.078567402179364698 -0.10215918191441409 0.10641426900994663 + + false + + + + /bodyset/pelvis + + -0.075736926584752007 -0.10578039587845123 -0.10336475585602306 + + false + + + + + + + + + + + diff --git a/ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/sessionMetadata.yaml b/ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/sessionMetadata.yaml new file mode 100644 index 00000000..608920c1 --- /dev/null +++ b/ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/sessionMetadata.yaml @@ -0,0 +1,20 @@ +calibrationSettings: + overwriteDeployedIntrinsics: false + saveSessionIntrinsics: false +checkerBoard: + black2BlackCornersHeight_n: 4 + black2BlackCornersWidth_n: 5 + placement: backWall + squareSideLength_mm: 35.0 +gender_mf: m +height_m: 1.86 +iphoneModel: + Cam0: iPhone13,3 + Cam1: iPhone13,3 + Cam2: iPhone13,3 +markerAugmentationSettings: + markerAugmenterModel: LSTM +mass_kg: 83.0 +openSimModel: LaiArnoldModified2017_poly_withArms_weldHand +posemodel: openpose +subjectID: sdu_benchmark diff --git a/Functions/gait_analysis.py b/ActivityAnalyses/LambdaFunctions/analyzeGait.py similarity index 82% rename from Functions/gait_analysis.py rename to ActivityAnalyses/LambdaFunctions/analyzeGait.py index 7c261f2c..fe86081e 100644 --- a/Functions/gait_analysis.py +++ b/ActivityAnalyses/LambdaFunctions/analyzeGait.py @@ -9,12 +9,13 @@ import os import sys -sys.path.append("..") +sys.path.append("../../") +sys.path.append("../ActivityClasses") -import utilsKinematics +from gaitAnalysis import gait_analysis import utils -def gait_analysis(trial_id,scalar_names=None,n_gait_cycles=1,filterFreq=6): +def analyzeGait(trial_id,scalar_names=None,n_gait_cycles=1,filterFreq=6): session_id = utils.get_trial_json(trial_id)['session'] @@ -25,10 +26,10 @@ def gait_analysis(trial_id,scalar_names=None,n_gait_cycles=1,filterFreq=6): trialName = utils.download_trial(trial_id,sessionDir,session_id=session_id) # init gait analysis - gait_r = utilsKinematics.gait_analysis(sessionDir, trialName, leg='r', + gait_r = gait_analysis(sessionDir, trialName, leg='r', lowpass_cutoff_frequency_for_coordinate_values=filterFreq, n_gait_cycles=n_gait_cycles) - gait_l = utilsKinematics.gait_analysis(sessionDir, trialName, leg='l', + gait_l = gait_analysis(sessionDir, trialName, leg='l', lowpass_cutoff_frequency_for_coordinate_values=filterFreq, n_gait_cycles=n_gait_cycles) @@ -57,7 +58,7 @@ def gait_analysis(trial_id,scalar_names=None,n_gait_cycles=1,filterFreq=6): scalar_names = {'gait_speed','stride_length','step_width'} -gaitResults = gait_analysis(trial_id,scalar_names=scalar_names, +gaitResults = analyzeGait(trial_id,scalar_names=scalar_names, n_gait_cycles=4) diff --git a/utilsKinematics.py b/utilsKinematics.py index 74d6fbc5..27562653 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -376,324 +376,4 @@ def get_center_of_mass_accelerations(self, lowpass_cutoff_frequency=-1): columns = ['time'] + ['x','y','z'] com_accelerations = pd.DataFrame(data=data, columns=columns) - return com_accelerations - -# %% -class gait_analysis(kinematics): - - def __init__(self, sessionDir, trialName, leg='auto', - lowpass_cutoff_frequency_for_coordinate_values=-1, - n_gait_cycles=1): - - # inherit init from kinematics class - super().__init__(sessionDir, trialName, - lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) - - # Marker data load and filter - trcFilePath = os.path.join(sessionDir,'MarkerData', - '{}.trc'.format(trialName)) - self.markerDict = trc_2_dict(trcFilePath) - if lowpass_cutoff_frequency_for_coordinate_values > 0: - self.markerDict['markers'] = { - marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency_for_coordinate_values) \ - for marker_name, data in self.markerDict['markers'].items()} - - # Coordinate values - self.coordinateValues = self.get_coordinate_values() - - # Segment gait cycles - self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles,leg=leg) - self.nGaitCycles = np.shape(self.gaitEvents['ipsilateralIdx'])[0] - - # determine treadmill speed (0 if overground) - self.treadmillSpeed = self.compute_treadmill_speed() - - # Compute COM trajectory and gait frame...used in multiple scalar computations - self.comValues = self.get_center_of_mass_values() - self.R_world_to_gait = self.compute_gait_frame() - - def compute_scalars(self,scalarNames): - - # verify that scalarNames are methods in gait_analysis - method_names = [func for func in dir(self) if callable(getattr(self, func))] - possibleMethods = [entry for entry in method_names if 'compute_' in entry] - - if scalarNames is None: - print('No scalars defined, these methods are available:') - print(*possibleMethods) - return - - nonexistant_methods = [entry for entry in scalarNames if 'compute_' + entry not in method_names] - - if len(nonexistant_methods) > 0: - raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') - - scalarDict = {} - for scalarName in scalarNames: - thisFunction = getattr(self, 'compute_' + scalarName) - scalarDict[scalarName] = thisFunction() - - return scalarDict - - def compute_stride_length(self): - - # get calc positions based on self.gaitEvents['leg'] from self.markerDict - if self.gaitEvents['ipsilateralLeg'] == 'r': - leg = 'r' - else: - leg = 'L' - calc_position = self.markerDict['markers'][leg + '_calc_study'] - - # find stride length on treadmill - # difference in ipsilateral calcaneus position at heel strike + treadmill speed * time - strideLength = np.linalg.norm(calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ - calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + \ - self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) - - # average across all strides - strideLength = np.mean(strideLength) - - return strideLength - - def compute_gait_speed(self): - - comValuesArray = np.vstack((self.comValues['x'],self.comValues['y'],self.comValues['z'])).T - gait_speed = (np.linalg.norm(comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ - comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2)) / \ - np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed - - # average across all strides - gait_speed = np.mean(gait_speed) - - return gait_speed - - def compute_treadmill_speed(self): - if self.gaitEvents['ipsilateralLeg'] == 'r': - leg = 'r' - else: - leg = 'L' - foot_position = self.markerDict['markers'][leg + '_ankle_study'] - - stanceTimeLength = np.round(np.diff(self.gaitEvents['ipsilateralIdx'][:,:2])) - startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.1*stanceTimeLength).astype(int) - endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceTimeLength).astype(int) - - # average instantaneous velocities - dt = np.diff(self.markerDict['time'][:2])[0] - for i in range(self.nGaitCycles): - footVel = np.linalg.norm(np.mean(np.diff( - foot_position[startIdx[i,0]:endIdx[i,0],:],axis=0),axis=0)/dt) - - treadmillSpeed = np.mean(footVel) - - # overground - if treadmillSpeed < .3: - treadmillSpeed = 0 - - return treadmillSpeed - - def compute_step_width(self): - # get ankle joint center positions - if self.gaitEvents['ipsilateralLeg'] == 'r': - leg = 'r' - contLeg = 'L' - else: - leg = 'L' - contLeg = 'r' - ankle_position_ips = (self.markerDict['markers'][leg + '_ankle_study'] + - self.markerDict['markers'][leg + '_mankle_study'])/2 - ankle_position_cont = (self.markerDict['markers'][contLeg + '_ankle_study'] + - self.markerDict['markers'][contLeg + '_mankle_study'])/2 - - ankleVector = ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - \ - ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]] - - ankleVector_inGaitFrame = np.array([np.dot(ankleVector[i,:], self.R_world_to_gait[i,:,:]) \ - for i in range(self.nGaitCycles)]) - - # step width is z distance - stepWidth = np.abs(ankleVector_inGaitFrame[:,2]) - - # average across gait cycles - stepWidth = np.mean(stepWidth) - - return stepWidth - - def compute_gait_frame(self): - # Create frame for each gait cycle with x: pelvis heading, - # z: average vector between ASIS during gait cycle, y: cross. - - # Pelvis center trajectory (for overground heading vector) - pelvisMarkerNames = ['r.ASIS_study','L.ASIS_study','r.PSIS_study','L.PSIS_study'] - pelvisMarkers = [self.markerDict['markers'][mkr] for mkr in pelvisMarkerNames] - pelvisCenter = np.mean(np.array(pelvisMarkers),axis=0) - - # ankle trajectory (for treadmill heading vector) - leg = self.gaitEvents['ipsilateralLeg'] - if leg == 'l': leg='L' - anklePos = self.markerDict['markers'][leg + '_ankle_study'] - - # vector from left ASIS to right ASIS (for mediolateral direction) - asisMarkerNames = ['L.ASIS_study','r.ASIS_study'] - asisMarkers = [self.markerDict['markers'][mkr] for mkr in asisMarkerNames] - asisVector = np.squeeze(np.diff(np.array(asisMarkers),axis=0)) - - # heading vector per gait cycle - # if overground, use pelvis center trajectory; treadmill: ankle trajectory - if self.treadmillSpeed == 0: - x = np.diff(pelvisCenter[self.gaitEvents['ipsilateralIdx'][:,(0,2)],:],axis=1)[:,0,:] - x = x / np.linalg.norm(x,axis=1,keepdims=True) - else: - x = np.zeros((self.nGaitCycles,3)) - for i in range(self.nGaitCycles): - x[i,:] = anklePos[self.gaitEvents['ipsilateralIdx'][i,2]] - \ - anklePos[self.gaitEvents['ipsilateralIdx'][i,1]] - x = x / np.linalg.norm(x,axis=1,keepdims=True) - - # mean ASIS vector over gait cycle - z = np.zeros((self.nGaitCycles,3)) - for i in range(self.nGaitCycles): - z[i,:] = np.mean(asisVector[self.gaitEvents['ipsilateralIdx'][i,0]: \ - self.gaitEvents['ipsilateralIdx'][i,2]],axis=0) - z = z / np.linalg.norm(z,axis=1,keepdims=True) - - # cross to get y - y = np.cross(z,x) - - # 3x3xnSteps - R_lab_to_gait = np.stack((x.T,y.T,z.T),axis=1).transpose((2, 0, 1)) - - return R_lab_to_gait - - def get_coordinates_normalized_time(self): - - colNames = self.coordinateValues.columns - data = self.coordinateValues.to_numpy(copy=True) - coordValuesNorm = [] - for i in range(self.nGaitCycles): - coordValues = data[self.gaitEvents['ipsilateralIdx'][i,0]:self.gaitEvents['ipsilateralIdx'][i,2]] - coordValuesNorm.append(np.stack([np.interp(np.linspace(0,100,101), - np.linspace(0,100,len(coordValues)),coordValues[:,i]) \ - for i in range(coordValues.shape[1])],axis=1)) - - coordinateValuesTimeNormalized = {} - # average - coordVals_mean = np.mean(np.array(coordValuesNorm),axis=0) - coordinateValuesTimeNormalized['mean'] = pd.DataFrame(data=coordVals_mean, columns=colNames) - - #return to dataframe - coordinateValuesTimeNormalized['indiv'] = [pd.DataFrame(data=d, columns=colNames) for d in coordValuesNorm] - - return coordinateValuesTimeNormalized - - def segment_walking(self, n_gait_cycles=1, leg='auto', visualize=False): - # subtract sacrum from foot - # visually, it looks like the position-based approach will be more robust - r_calc_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ - 'markers']['r.PSIS_study'])[:,0] - r_toe_rel_x = (self.markerDict['markers']['r_toe_study'] - self.markerDict[ - 'markers']['r.PSIS_study'])[:,0] - - # repeat for left - l_calc_rel_x = (self.markerDict['markers']['L_calc_study'] - self.markerDict[ - 'markers']['L.PSIS_study'])[:,0] - l_toe_rel_x = (self.markerDict['markers']['L_toe_study'] - self.markerDict[ - 'markers']['L.PSIS_study'])[:,0] - - # Find HS - rHS, _ = find_peaks(r_calc_rel_x) - lHS, _ = find_peaks(l_calc_rel_x) - - # Find TO - rTO, _ = find_peaks(-r_toe_rel_x) - lTO, _ = find_peaks(-l_toe_rel_x) - - if visualize==True: - import matplotlib.pyplot as plt - plt.close('all') - plt.figure(1) - plt.plot(self.markerDict['time'],r_toe_rel_x,label='toe') - plt.plot(self.markerDict['time'],r_calc_rel_x,label='calc') - plt.scatter(self.markerDict['time'][rHS], r_calc_rel_x[rHS], color='red', label='rHS') - plt.scatter(self.markerDict['time'][rTO], r_toe_rel_x[rTO], color='blue', label='rTO') - plt.legend() - - plt.figure(2) - plt.plot(self.markerDict['time'],l_toe_rel_x,label='toe') - plt.plot(self.markerDict['time'],l_calc_rel_x,label='calc') - plt.scatter(self.markerDict['time'][lHS], l_calc_rel_x[lHS], color='red', label='lHS') - plt.scatter(self.markerDict['time'][lTO], l_toe_rel_x[lTO], color='blue', label='lTO') - plt.legend() - - - # find the number of gait cycles for the foot of interest - if leg=='auto': - # find the last HS of either foot - if rHS[-1] > lHS[-1]: - leg = 'r' - else: - leg = 'l' - - # find the number of gait cycles for the foot of interest - if leg == 'r': - hsIps = rHS - toIps = rTO - hsCont = lHS - toCont = lTO - elif leg == 'l': - hsIps = lHS - toIps = lTO - hsCont = rHS - toCont = rTO - - n_gait_cycles = np.min([n_gait_cycles, len(hsIps)-1]) - gaitEvents_ips = np.zeros((n_gait_cycles, 3),dtype=np.int) - gaitEvents_cont = np.zeros((n_gait_cycles, 2),dtype=np.int) - if n_gait_cycles <1: - raise Exception('Not enough gait cycles found.') - - for i in range(n_gait_cycles): - # ipsilateral HS, TO, HS - gaitEvents_ips[i,0] = hsIps[-i-2] - gaitEvents_ips[i,2] = hsIps[-i-1] - - # iterate in reverse through ipsilateral TO, finding the one that is within the range of gaitEvents_ips - toIpsFound = False - for j in range(len(toIps)): - if toIps[-j-1] > gaitEvents_ips[i,0] and toIps[-j-1] < gaitEvents_ips[i,2] and not toIpsFound: - gaitEvents_ips[i,1] = toIps[-j-1] - toIpsFound = True - - # contralateral TO, HS - # iterate in reverse through contralateral HS and TO, finding the one that is within the range of gaitEvents_ips - hsContFound = False - toContFound = False - for j in range(len(toCont)): - if toCont[-j-1] > gaitEvents_ips[i,0] and toCont[-j-1] < gaitEvents_ips[i,2] and not toContFound: - gaitEvents_cont[i,0] = toCont[-j-1] - toContFound = True - - for j in range(len(hsCont)): - if hsCont[-j-1] > gaitEvents_ips[i,0] and hsCont[-j-1] < gaitEvents_ips[i,2] and not hsContFound: - gaitEvents_cont[i,1] = hsCont[-j-1] - hsContFound = True - - # making contralateral gait events optional - if not toContFound or not hsContFound: - raise Warning('Could not find contralateral gait event within ipsilateral gait event range.') - gaitEvents_cont[i,0] = np.nan - gaitEvents_cont[i,1] = np.nan - - # convert gaitEvents to times using self.markerDict['time'] - gaitEventTimes_ips = self.markerDict['time'][gaitEvents_ips] - gaitEventTimes_cont = self.markerDict['time'][gaitEvents_cont] - - gaitEvents = {'ipsilateralIdx':gaitEvents_ips, - 'contralateralIdx':gaitEvents_cont, - 'ipsilateralTime':gaitEventTimes_ips, - 'contralateralTime':gaitEventTimes_cont, - 'eventNamesIpsilateral':['HS','TO','HS'], - 'eventNamesContralateral':['TO','HS'], - 'ipsilateralLeg':leg} - - return gaitEvents \ No newline at end of file + return com_accelerations \ No newline at end of file diff --git a/utilsProcessing.py b/utilsProcessing.py index 995e9262..ee76996a 100644 --- a/utilsProcessing.py +++ b/utilsProcessing.py @@ -18,6 +18,9 @@ limitations under the License. ''' +import sys +sys.path.append('AnalysisFunctions/ActivityClasses/') + import os import logging import opensim @@ -38,7 +41,7 @@ def lowPassFilter(time, data, lowpass_cutoff_frequency, order=4): # %% Segment gait def segment_gait(session_id, trial_name, dataFolder, gait_cycles_from_end=0): - from utilsKinematics import gait_analysis + from gaitAnalysis import gait_analysis trial_id = getTrialId(session_id,trial_name) download_trial(trial_id,os.path.join(dataFolder,session_id),session_id=session_id) From cdd4f41aed4f7ffccdd580d1ffbc468baa42758f Mon Sep 17 00:00:00 2001 From: suhlrich Date: Mon, 28 Aug 2023 08:51:44 -0700 Subject: [PATCH 11/29] compute: single support, double support, cadence --- .../ActivityClasses/gaitAnalysis.py | 30 +++++++++++++++++++ .../LambdaFunctions/analyzeGait.py | 3 +- 2 files changed, 32 insertions(+), 1 deletion(-) diff --git a/ActivityAnalyses/ActivityClasses/gaitAnalysis.py b/ActivityAnalyses/ActivityClasses/gaitAnalysis.py index 14192b83..36838aef 100644 --- a/ActivityAnalyses/ActivityClasses/gaitAnalysis.py +++ b/ActivityAnalyses/ActivityClasses/gaitAnalysis.py @@ -120,6 +120,16 @@ def compute_gait_speed(self): return gait_speed + def compute_cadence(self): + + # in steps per second + cadence = 1/np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + + # average across strides + cadence = np.mean(cadence) + + return cadence + def compute_treadmill_speed(self): if self.gaitEvents['ipsilateralLeg'] == 'r': leg = 'r' @@ -171,6 +181,26 @@ def compute_step_width(self): stepWidth = np.mean(stepWidth) return stepWidth + + def compute_single_support_time(self): + + singleSupportTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) + + # average across strides + singleSupportTime = np.mean(singleSupportTime) + + return singleSupportTime + + def compute_double_support_time(self): + + # ipsilateral single support time - contralateral swing time + doubleSupportTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - \ + np.diff(self.gaitEvents['contralateralTime'][:,:2]) + + # average across strides + doubleSupportTime = np.mean(doubleSupportTime) + + return doubleSupportTime def compute_gait_frame(self): # Create frame for each gait cycle with x: pelvis heading, diff --git a/ActivityAnalyses/LambdaFunctions/analyzeGait.py b/ActivityAnalyses/LambdaFunctions/analyzeGait.py index fe86081e..25662d90 100644 --- a/ActivityAnalyses/LambdaFunctions/analyzeGait.py +++ b/ActivityAnalyses/LambdaFunctions/analyzeGait.py @@ -56,7 +56,8 @@ def analyzeGait(trial_id,scalar_names=None,n_gait_cycles=1,filterFreq=6): # trial_name = 'walk_1_25ms' # trial_id = utils.getTrialId(session_id,trial_name) -scalar_names = {'gait_speed','stride_length','step_width'} +scalar_names = {'gait_speed','stride_length','step_width','cadence', + 'single_support_time','double_support_time'} gaitResults = analyzeGait(trial_id,scalar_names=scalar_names, n_gait_cycles=4) From 59d5855868edf7f696d321b819b9f78cfe8ac88c Mon Sep 17 00:00:00 2001 From: suhlrich Date: Mon, 28 Aug 2023 09:06:07 -0700 Subject: [PATCH 12/29] remove data --- .gitignore | 1 + .../MarkerData/walk.trc | 228 - .../OpenSimData/Kinematics/walk.mot | 233 - ...ied2017_poly_withArms_weldHand_scaled.osim | 14806 ---------------- .../sessionMetadata.yaml | 20 - 5 files changed, 1 insertion(+), 15287 deletions(-) delete mode 100644 ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/MarkerData/walk.trc delete mode 100644 ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/OpenSimData/Kinematics/walk.mot delete mode 100644 ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/OpenSimData/Model/LaiArnoldModified2017_poly_withArms_weldHand_scaled.osim delete mode 100644 ActivityAnalyses/Data/03284efb-2244-4a48-aec9-abc34afdffc8/sessionMetadata.yaml diff --git a/.gitignore b/.gitignore index b36d5e8e..3770de95 100644 --- a/.gitignore +++ b/.gitignore @@ -3,6 +3,7 @@ __pycache__ *.log *.ipynb_checkpoints Data/* +*/Data/* *DS_Store diff --git 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overwriteDeployedIntrinsics: false - saveSessionIntrinsics: false -checkerBoard: - black2BlackCornersHeight_n: 4 - black2BlackCornersWidth_n: 5 - placement: backWall - squareSideLength_mm: 35.0 -gender_mf: m -height_m: 1.86 -iphoneModel: - Cam0: iPhone13,3 - Cam1: iPhone13,3 - Cam2: iPhone13,3 -markerAugmentationSettings: - markerAugmenterModel: LSTM -mass_kg: 83.0 -openSimModel: LaiArnoldModified2017_poly_withArms_weldHand -posemodel: openpose -subjectID: sdu_benchmark From deea483173336e8d32eb42b9d58e2f96ff069ae4 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Thu, 31 Aug 2023 12:17:53 -0700 Subject: [PATCH 13/29] remove imports and explicit model path --- utils.py | 11 +++++++++++ utilsKinematics.py | 7 +++---- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/utils.py b/utils.py index d92371cf..7e35c2eb 100644 --- a/utils.py +++ b/utils.py @@ -136,6 +136,17 @@ def get_model_and_metadata(session_id, session_path): return modelName +def get_model_from_metadata(sessionFolder,appendText='_scaled'): + metadataPath = os.path.join(sessionFolder,'sessionMetadata.yaml') + + if os.path.exists(metadataPath): + metadata = import_metadata(os.path.join(sessionFolder,'sessionMetadata.yaml')) + modelFile = metadata['openSimModel'] + appendText + '.osim' + else: + raise Exception('Session metadata not found, could not identify OpenSim model.') + + return modelFile + def get_motion_data(trial_id, session_path): trial = get_trial_json(trial_id) diff --git a/utilsKinematics.py b/utilsKinematics.py index 27562653..d51eac6d 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -19,16 +19,14 @@ ''' import os -import glob import opensim +import utils import numpy as np import pandas as pd import scipy.interpolate as interpolate -from scipy.signal import find_peaks from utilsProcessing import lowPassFilter -from utilsTRC import trc_2_dict class kinematics: @@ -45,7 +43,8 @@ def __init__(self, sessionDir, trialName, modelPath = os.path.join(sessionDir, 'OpenSimData', 'Model', '{}.osim'.format(modelName)) if not os.path.exists(modelPath): - modelPath = glob.glob(os.path.join(os.path.dirname(modelPath),'*.osim'))[0] + modelFromMetadata = utils.get_model_from_metadata(sessionDir) + modelPath = modelPath.replace(modelName + '.osim',modelFromMetadata) self.model = opensim.Model(modelPath) self.model.initSystem() From 12bac54e8d24de31aefebf9cddd2d3decf936329 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Thu, 31 Aug 2023 12:25:57 -0700 Subject: [PATCH 14/29] add _ to function names --- ActivityAnalyses/LambdaFunctions/analyzeGait.py | 2 +- UtilsDynamicSimulations/OpenSimAD/utilsOpenSimAD.py | 12 ++++++------ utils.py | 2 +- utilsProcessing.py | 8 ++++---- 4 files changed, 12 insertions(+), 12 deletions(-) diff --git a/ActivityAnalyses/LambdaFunctions/analyzeGait.py b/ActivityAnalyses/LambdaFunctions/analyzeGait.py index 25662d90..ea93b07e 100644 --- a/ActivityAnalyses/LambdaFunctions/analyzeGait.py +++ b/ActivityAnalyses/LambdaFunctions/analyzeGait.py @@ -54,7 +54,7 @@ def analyzeGait(trial_id,scalar_names=None,n_gait_cycles=1,filterFreq=6): # treadmill trial 1.25m/s # session_id = "4d5c3eb1-1a59-4ea1-9178-d3634610561c" # trial_name = 'walk_1_25ms' -# trial_id = utils.getTrialId(session_id,trial_name) +# trial_id = utils.get_trial_id(session_id,trial_name) scalar_names = {'gait_speed','stride_length','step_width','cadence', 'single_support_time','double_support_time'} diff --git a/UtilsDynamicSimulations/OpenSimAD/utilsOpenSimAD.py b/UtilsDynamicSimulations/OpenSimAD/utilsOpenSimAD.py index ddd3072f..7cf4def3 100644 --- a/UtilsDynamicSimulations/OpenSimAD/utilsOpenSimAD.py +++ b/UtilsDynamicSimulations/OpenSimAD/utilsOpenSimAD.py @@ -37,8 +37,8 @@ from utils import (storage_to_numpy, storage_to_dataframe, download_kinematics, import_metadata, numpy_to_storage) -from utilsProcessing import (segmentSquats, segmentSTS, adjustMuscleWrapping, - generateModelWithContacts) +from utilsProcessing import (segment_squats, segment_STS, adjust_muscle_wrapping, + generate_model_with_contacts) from settingsOpenSimAD import get_setup # %% Filter numpy array. @@ -2179,10 +2179,10 @@ def processInputsOpenSimAD(baseDir, dataFolder, session_id, trial_name, # Prepare inputs for dynamic simulations. # Adjust muscle wrapping. - adjustMuscleWrapping(baseDir, dataFolder, session_id, + adjust_muscle_wrapping(baseDir, dataFolder, session_id, OpenSimModel=OpenSimModel, overwrite=overwrite) # Add foot-ground contacts to musculoskeletal model. - generateModelWithContacts(dataFolder, session_id, + generate_model_with_contacts(dataFolder, session_id, OpenSimModel=OpenSimModel, overwrite=overwrite) # Generate external function. generateExternalFunction(baseDir, dataFolder, session_id, @@ -2198,9 +2198,9 @@ def processInputsOpenSimAD(baseDir, dataFolder, session_id, trial_name, if (repetition is not None and (motion_type == 'squats' or motion_type == 'sit_to_stand')): if motion_type == 'squats': - times_window = segmentSquats(pathMotionFile, visualize=True) + times_window = segment_squats(pathMotionFile, visualize=True) elif motion_type == 'sit_to_stand': - _, _, times_window = segmentSTS(pathMotionFile, visualize=True) + _, _, times_window = segment_STS(pathMotionFile, visualize=True) time_window = times_window[repetition] settings['repetition'] = repetition else: diff --git a/utils.py b/utils.py index 7e35c2eb..3393c905 100644 --- a/utils.py +++ b/utils.py @@ -273,7 +273,7 @@ def download_trial(trial_id, folder, session_id=None): return trial['name'] # %% get trialID from name -def getTrialId(session_id,trial_name): +def get_trial_id(session_id,trial_name): session = get_session_json(session_id) trial_id = [t['id'] for t in session['trials'] if t['name'] == trial_name] diff --git a/utilsProcessing.py b/utilsProcessing.py index ee76996a..728e57c4 100644 --- a/utilsProcessing.py +++ b/utilsProcessing.py @@ -51,7 +51,7 @@ def segment_gait(session_id, trial_name, dataFolder, gait_cycles_from_end=0): return heelstrikeTimes, gait # %% Segment squats. -def segmentSquats(ikFilePath, pelvis_ty=None, timeVec=None, visualize=False, +def segment_squats(ikFilePath, pelvis_ty=None, timeVec=None, visualize=False, filter_pelvis_ty=True, cutoff_frequency=4, height=.2): # Extract pelvis_ty if not given. @@ -112,7 +112,7 @@ def segmentSquats(ikFilePath, pelvis_ty=None, timeVec=None, visualize=False, from delayed start to corresponding periodic end in terms of vertical pelvis position. ''' -def segmentSTS(ikFilePath, pelvis_ty=None, timeVec=None, velSeated=0.3, +def segment_STS(ikFilePath, pelvis_ty=None, timeVec=None, velSeated=0.3, velStanding=0.15, visualize=False, filter_pelvis_ty=True, cutoff_frequency=4, delay=0.1): @@ -219,7 +219,7 @@ def segmentSTS(ikFilePath, pelvis_ty=None, timeVec=None, velSeated=0.3, # wrapping giving rise to bad muscle-tendon lengths and moment arms. Changes # are made for the gmax1, iliacus, and psoas. Changes are documented in # modelAdjustment.log. -def adjustMuscleWrapping( +def adjust_muscle_wrapping( baseDir, dataDir, subject, poseDetector='DefaultPD', cameraSetup='DefaultModel', OpenSimModel="LaiUhlrich2022", overwrite=False): @@ -495,7 +495,7 @@ def getMomentArms(model, poses, muscleName, coordinateForMomentArm): return momentArms # %% Generate model with contacts. -def generateModelWithContacts( +def generate_model_with_contacts( dataDir, subject, poseDetector='DefaultPD', cameraSetup='DefaultModel', OpenSimModel="LaiUhlrich2022", setPatellaMasstoZero=True, overwrite=False): From 25fc3fa94fb82abd4afdc470f41818c6fb3b924d Mon Sep 17 00:00:00 2001 From: suhlrich Date: Thu, 31 Aug 2023 13:22:19 -0700 Subject: [PATCH 15/29] lazy loading --- .../ActivityClasses/gaitAnalysis.py | 32 ++++++++++++++----- .../LambdaFunctions/analyzeGait.py | 8 ++--- utilsKinematics.py | 32 +++++++++++-------- utilsProcessing.py | 10 +++--- 4 files changed, 52 insertions(+), 30 deletions(-) diff --git a/ActivityAnalyses/ActivityClasses/gaitAnalysis.py b/ActivityAnalyses/ActivityClasses/gaitAnalysis.py index 36838aef..0b69a134 100644 --- a/ActivityAnalyses/ActivityClasses/gaitAnalysis.py +++ b/ActivityAnalyses/ActivityClasses/gaitAnalysis.py @@ -61,10 +61,23 @@ def __init__(self, sessionDir, trialName, leg='auto', # determine treadmill speed (0 if overground) self.treadmillSpeed = self.compute_treadmill_speed() - # Compute COM trajectory and gait frame...used in multiple scalar computations - self.comValues = self.get_center_of_mass_values() - self.R_world_to_gait = self.compute_gait_frame() - + # initialize variables to be lazy loaded + self._comValues = None + self._R_world_to_gait = None + + # # Lazy loaded variables (setting state trajectory in utilsKinematics is slow) + # Compute COM trajectory and gait frame...used in multiple scalar computations + def comValues(self): + if self._comValues is None: + self._comValues = self.get_center_of_mass_values() + return self._comValues + + def R_world_to_gait(self): + if self._R_world_to_gait is None: + self._R_world_to_gait = self.compute_gait_frame() + return self._R_world_to_gait + # # End Lazy loading + def compute_scalars(self,scalarNames): # verify that scalarNames are methods in gait_analysis @@ -110,7 +123,7 @@ def compute_stride_length(self): def compute_gait_speed(self): - comValuesArray = np.vstack((self.comValues['x'],self.comValues['y'],self.comValues['z'])).T + comValuesArray = np.vstack((self.comValues()['x'],self.comValues()['y'],self.comValues()['z'])).T gait_speed = (np.linalg.norm(comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2)) / \ np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed @@ -171,7 +184,7 @@ def compute_step_width(self): ankleVector = ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - \ ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]] - ankleVector_inGaitFrame = np.array([np.dot(ankleVector[i,:], self.R_world_to_gait[i,:,:]) \ + ankleVector_inGaitFrame = np.array([np.dot(ankleVector[i,:], self.R_world_to_gait()[i,:,:]) \ for i in range(self.nGaitCycles)]) # step width is z distance @@ -201,7 +214,7 @@ def compute_double_support_time(self): doubleSupportTime = np.mean(doubleSupportTime) return doubleSupportTime - + def compute_gait_frame(self): # Create frame for each gait cycle with x: pelvis heading, # z: average vector between ASIS during gait cycle, y: cross. @@ -378,4 +391,7 @@ def segment_walking(self, n_gait_cycles=1, leg='auto', visualize=False): 'eventNamesContralateral':['TO','HS'], 'ipsilateralLeg':leg} - return gaitEvents \ No newline at end of file + return gaitEvents + + # Lazy loading some things that are computationally expensive + \ No newline at end of file diff --git a/ActivityAnalyses/LambdaFunctions/analyzeGait.py b/ActivityAnalyses/LambdaFunctions/analyzeGait.py index ea93b07e..b4d92ddc 100644 --- a/ActivityAnalyses/LambdaFunctions/analyzeGait.py +++ b/ActivityAnalyses/LambdaFunctions/analyzeGait.py @@ -49,12 +49,12 @@ def analyzeGait(trial_id,scalar_names=None,n_gait_cycles=1,filterFreq=6): # TODO delete. Testing as script # overground trial -trial_id = 'bf181007-d0f3-4395-8dc3-a0f0e5553761' +# trial_id = 'bf181007-d0f3-4395-8dc3-a0f0e5553761' # treadmill trial 1.25m/s -# session_id = "4d5c3eb1-1a59-4ea1-9178-d3634610561c" -# trial_name = 'walk_1_25ms' -# trial_id = utils.get_trial_id(session_id,trial_name) +session_id = "4d5c3eb1-1a59-4ea1-9178-d3634610561c" +trial_name = 'walk_1_25ms' +trial_id = utils.get_trial_id(session_id,trial_name) scalar_names = {'gait_speed','stride_length','step_width','cadence', 'single_support_time','double_support_time'} diff --git a/utilsKinematics.py b/utilsKinematics.py index d51eac6d..d591d1c8 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -60,6 +60,9 @@ def __init__(self, sessionDir, trialName, tableProcessor.append(opensim.TabOpUseAbsoluteStateNames()) self.time = np.asarray(self.table.getIndependentColumn()) + # initialize the state trajectory. We will set it in other functions + # if it is needed + self._stateTrajectory = None # Filter coordinate values. if lowpass_cutoff_frequency_for_coordinate_values > 0: @@ -108,11 +111,7 @@ def __init__(self, sessionDir, trialName, if not stateVariableNameStr in existingLabels: vec_0 = opensim.Vector([0] * self.table.getNumRows()) self.table.appendColumn(stateVariableNameStr, vec_0) - - # Set state trajectory - self.stateTrajectory = opensim.StatesTrajectory.createFromStatesTable( - self.model, self.table) - + # Number of muscles. self.nMuscles = 0 self.forceSet = self.model.getForceSet() @@ -147,7 +146,14 @@ def __init__(self, sessionDir, trialName, 'elbow_flex_r', 'pro_sup_r', 'arm_flex_l', 'arm_add_l', 'arm_rot_l', 'elbow_flex_l', 'pro_sup_l'] - + + # Only set the state trajectory when needed b/c it is slow + def stateTrajectory(self): + if self._stateTrajectory is None: + self._stateTrajectory = opensim.StatesTrajectory.createFromStatesTable( + self.model, self.table) + return self._stateTrajectory + def get_coordinate_values(self, in_degrees=True, lowpass_cutoff_frequency=-1): @@ -229,14 +235,14 @@ def get_muscle_tendon_lengths(self, lowpass_cutoff_frequency=-1): # Compute muscle-tendon lengths. lMT = np.zeros((self.table.getNumRows(), self.nMuscles)) for i in range(self.table.getNumRows()): - self.model.realizePosition(self.stateTrajectory[i]) + self.model.realizePosition(self.stateTrajectory()[i]) if i == 0: muscleNames = [] for m in range(self.forceSet.getSize()): c_force_elt = self.forceSet.get(m) if 'Muscle' in c_force_elt.getConcreteClassName(): cObj = opensim.Muscle.safeDownCast(c_force_elt) - lMT[i,m] = cObj.getLength(self.stateTrajectory[i]) + lMT[i,m] = cObj.getLength(self.stateTrajectory()[i]) if i == 0: muscleNames.append(c_force_elt.getName()) @@ -258,7 +264,7 @@ def get_moment_arms(self, lowpass_cutoff_frequency=-1): dM = np.zeros((self.table.getNumRows(), self.nMuscles, self.nCoordinates)) for i in range(self.table.getNumRows()): - self.model.realizePosition(self.stateTrajectory[i]) + self.model.realizePosition(self.stateTrajectory()[i]) if i == 0: muscleNames = [] for m in range(self.forceSet.getSize()): @@ -285,7 +291,7 @@ def get_moment_arms(self, lowpass_cutoff_frequency=-1): coordinate = self.coordinateSet.get( self.coordinates.index(coord)) dM[i, m, c] = cObj.computeMomentArm( - self.stateTrajectory[i], coordinate) + self.stateTrajectory()[i], coordinate) # Clean numerical artefacts (ie, moment arms smaller than 1e-5 m). dM[np.abs(dM) < 1e-5] = 0 @@ -312,11 +318,11 @@ def compute_center_of_mass(self): self.com_values = np.zeros((self.table.getNumRows(),3)) self.com_speeds = np.zeros((self.table.getNumRows(),3)) for i in range(self.table.getNumRows()): - self.model.realizeVelocity(self.stateTrajectory[i]) + self.model.realizeVelocity(self.stateTrajectory()[i]) self.com_values[i,:] = self.model.calcMassCenterPosition( - self.stateTrajectory[i]).to_numpy() + self.stateTrajectory()[i]).to_numpy() self.com_speeds[i,:] = self.model.calcMassCenterVelocity( - self.stateTrajectory[i]).to_numpy() + self.stateTrajectory()[i]).to_numpy() def get_center_of_mass_values(self, lowpass_cutoff_frequency=-1): diff --git a/utilsProcessing.py b/utilsProcessing.py index 728e57c4..6b09ea3d 100644 --- a/utilsProcessing.py +++ b/utilsProcessing.py @@ -27,7 +27,7 @@ import numpy as np from scipy import signal import matplotlib.pyplot as plt -from utils import storage_to_dataframe, download_trial, getTrialId +from utils import storage_to_dataframe, download_trial, get_trial_id def lowPassFilter(time, data, lowpass_cutoff_frequency, order=4): @@ -39,13 +39,13 @@ def lowPassFilter(time, data, lowpass_cutoff_frequency, order=4): return dataFilt # %% Segment gait -def segment_gait(session_id, trial_name, dataFolder, gait_cycles_from_end=0): +def segment_gait(session_id, trial_name, data_folder, gait_cycles_from_end=0): from gaitAnalysis import gait_analysis - trial_id = getTrialId(session_id,trial_name) - download_trial(trial_id,os.path.join(dataFolder,session_id),session_id=session_id) - gait = gait_analysis(os.path.join(dataFolder,session_id), trial_name,n_gait_cycles=4) + trial_id = get_trial_id(session_id,trial_name) + download_trial(trial_id,os.path.join(data_folder,session_id),session_id=session_id) + gait = gait_analysis(os.path.join(data_folder,session_id), trial_name,n_gait_cycles=4) heelstrikeTimes = gait.gaitEvents['ipsilateralTime'][gait_cycles_from_end,(0,2)].tolist() return heelstrikeTimes, gait From 6df279adb1688fda4ca330f560c62f1709423612 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Thu, 31 Aug 2023 15:40:04 -0700 Subject: [PATCH 16/29] add plots and rearrange files --- .../LambdaFunctions/analyzeGait.py | 67 ----------- .../gaitAnalysis.py => gait_analysis.py} | 67 +++++++---- Examples/example_gait_analysis.py | 108 ++++++++++++++++++ utils.py | 58 ++++++++++ utilsProcessing.py | 7 +- 5 files changed, 214 insertions(+), 93 deletions(-) delete mode 100644 ActivityAnalyses/LambdaFunctions/analyzeGait.py rename ActivityAnalyses/{ActivityClasses/gaitAnalysis.py => gait_analysis.py} (91%) create mode 100644 Examples/example_gait_analysis.py diff --git a/ActivityAnalyses/LambdaFunctions/analyzeGait.py b/ActivityAnalyses/LambdaFunctions/analyzeGait.py deleted file mode 100644 index b4d92ddc..00000000 --- a/ActivityAnalyses/LambdaFunctions/analyzeGait.py +++ /dev/null @@ -1,67 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Wed Jul 5 13:23:12 2023 - -this function performs kinematic gait analysis - -@authors: Scott Uhlrich & Antoine Falisse -""" - -import os -import sys -sys.path.append("../../") -sys.path.append("../ActivityClasses") - -from gaitAnalysis import gait_analysis -import utils - -def analyzeGait(trial_id,scalar_names=None,n_gait_cycles=1,filterFreq=6): - - session_id = utils.get_trial_json(trial_id)['session'] - - # Local data dir -> will be deleted with lambda instance - sessionDir = os.path.join(os.path.abspath('../Data'),session_id) - - # download data - trialName = utils.download_trial(trial_id,sessionDir,session_id=session_id) - - # init gait analysis - gait_r = gait_analysis(sessionDir, trialName, leg='r', - lowpass_cutoff_frequency_for_coordinate_values=filterFreq, - n_gait_cycles=n_gait_cycles) - gait_l = gait_analysis(sessionDir, trialName, leg='l', - lowpass_cutoff_frequency_for_coordinate_values=filterFreq, - n_gait_cycles=n_gait_cycles) - - # compute scalars and get time-normalized kinematics - gaitResults = {} - gaitResults['scalars_r'] = gait_r.compute_scalars(scalar_names) - gaitResults['curves_r'] = gait_r.get_coordinates_normalized_time() - gaitResults['scalars_l'] = gait_l.compute_scalars(scalar_names) - gaitResults['curves_l'] = gait_l.get_coordinates_normalized_time() - - # TODO post results to server - - # TODO delete return once posted to server - return gaitResults - - -# TODO delete. Testing as script - -# overground trial -# trial_id = 'bf181007-d0f3-4395-8dc3-a0f0e5553761' - -# treadmill trial 1.25m/s -session_id = "4d5c3eb1-1a59-4ea1-9178-d3634610561c" -trial_name = 'walk_1_25ms' -trial_id = utils.get_trial_id(session_id,trial_name) - -scalar_names = {'gait_speed','stride_length','step_width','cadence', - 'single_support_time','double_support_time'} - -gaitResults = analyzeGait(trial_id,scalar_names=scalar_names, - n_gait_cycles=4) - - - - diff --git a/ActivityAnalyses/ActivityClasses/gaitAnalysis.py b/ActivityAnalyses/gait_analysis.py similarity index 91% rename from ActivityAnalyses/ActivityClasses/gaitAnalysis.py rename to ActivityAnalyses/gait_analysis.py index 0b69a134..6673b862 100644 --- a/ActivityAnalyses/ActivityClasses/gaitAnalysis.py +++ b/ActivityAnalyses/gait_analysis.py @@ -102,12 +102,9 @@ def compute_scalars(self,scalarNames): return scalarDict def compute_stride_length(self): - - # get calc positions based on self.gaitEvents['leg'] from self.markerDict - if self.gaitEvents['ipsilateralLeg'] == 'r': - leg = 'r' - else: - leg = 'L' + + leg,_ = self.get_leg() + calc_position = self.markerDict['markers'][leg + '_calc_study'] # find stride length on treadmill @@ -144,10 +141,9 @@ def compute_cadence(self): return cadence def compute_treadmill_speed(self): - if self.gaitEvents['ipsilateralLeg'] == 'r': - leg = 'r' - else: - leg = 'L' + + leg,_ = self.get_leg() + foot_position = self.markerDict['markers'][leg + '_ankle_study'] stanceTimeLength = np.round(np.diff(self.gaitEvents['ipsilateralIdx'][:,:2])) @@ -170,12 +166,8 @@ def compute_treadmill_speed(self): def compute_step_width(self): # get ankle joint center positions - if self.gaitEvents['ipsilateralLeg'] == 'r': - leg = 'r' - contLeg = 'L' - else: - leg = 'L' - contLeg = 'r' + leg,contLeg = self.get_leg() + ankle_position_ips = (self.markerDict['markers'][leg + '_ankle_study'] + self.markerDict['markers'][leg + '_mankle_study'])/2 ankle_position_cont = (self.markerDict['markers'][contLeg + '_ankle_study'] + @@ -261,6 +253,16 @@ def compute_gait_frame(self): return R_lab_to_gait + def get_leg(self): + if self.gaitEvents['ipsilateralLeg'] == 'r': + leg = 'r' + contLeg = 'L' + else: + leg = 'L' + contLeg = 'r' + + return leg, contLeg + def get_coordinates_normalized_time(self): colNames = self.coordinateValues.columns @@ -277,12 +279,22 @@ def get_coordinates_normalized_time(self): coordVals_mean = np.mean(np.array(coordValuesNorm),axis=0) coordinateValuesTimeNormalized['mean'] = pd.DataFrame(data=coordVals_mean, columns=colNames) + # standard deviation + if self.nGaitCycles >2: + coordVals_sd = np.std(np.array(coordValuesNorm), axis=0) + coordinateValuesTimeNormalized['sd'] = pd.DataFrame(data=coordVals_sd, columns=colNames) + else: + coordinateValuesTimeNormalized['sd'] = None + #return to dataframe coordinateValuesTimeNormalized['indiv'] = [pd.DataFrame(data=d, columns=colNames) for d in coordValuesNorm] return coordinateValuesTimeNormalized - def segment_walking(self, n_gait_cycles=1, leg='auto', visualize=False): + def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): + # n_gait_cycles = -1 finds all accessible gait cycles. Otherwise, it finds + # that many gait cycles, working backwards from end of trial. + # subtract sacrum from foot # visually, it looks like the position-based approach will be more robust r_calc_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ @@ -297,14 +309,14 @@ def segment_walking(self, n_gait_cycles=1, leg='auto', visualize=False): 'markers']['L.PSIS_study'])[:,0] # Find HS - rHS, _ = find_peaks(r_calc_rel_x) - lHS, _ = find_peaks(l_calc_rel_x) + rHS, _ = find_peaks(r_calc_rel_x, prominence=0.3) + lHS, _ = find_peaks(l_calc_rel_x, prominence=0.3) # Find TO - rTO, _ = find_peaks(-r_toe_rel_x) - lTO, _ = find_peaks(-l_toe_rel_x) + rTO, _ = find_peaks(-r_toe_rel_x, prominence=0.3) + lTO, _ = find_peaks(-l_toe_rel_x, prominence=0.3) - if visualize==True: + if visualize: import matplotlib.pyplot as plt plt.close('all') plt.figure(1) @@ -341,8 +353,17 @@ def segment_walking(self, n_gait_cycles=1, leg='auto', visualize=False): hsCont = rHS toCont = rTO - n_gait_cycles = np.min([n_gait_cycles, len(hsIps)-1]) + if len(hsIps)-1 < n_gait_cycles: + print('You requested {} gait cycles, but only {} were found. Proceeding with this number.'.format( + n_gait_cycles,len(hsIps)-1)) + n_gait_cycles = len(hsIps)-1 + if n_gait_cycles == -1: + n_gait_cycles = len(hsIps)-1 + print('Processing {} gait cycles.'.format(n_gait_cycles)) + + # Ipsilateral gait events: heel strike, toe-off, heel strike gaitEvents_ips = np.zeros((n_gait_cycles, 3),dtype=np.int) + # Contralateral gait events: toe-off, heel strike gaitEvents_cont = np.zeros((n_gait_cycles, 2),dtype=np.int) if n_gait_cycles <1: raise Exception('Not enough gait cycles found.') diff --git a/Examples/example_gait_analysis.py b/Examples/example_gait_analysis.py new file mode 100644 index 00000000..916e76f9 --- /dev/null +++ b/Examples/example_gait_analysis.py @@ -0,0 +1,108 @@ +''' + --------------------------------------------------------------------------- + OpenCap processing: example_gait_analysis.py + --------------------------------------------------------------------------- + Copyright 2023 Stanford University and the Authors + + Author(s): Scott Uhlrich + + Licensed under the Apache License, Version 2.0 (the "License"); you may not + use this file except in compliance with the License. You may obtain a copy + of the License at http://www.apache.org/licenses/LICENSE-2.0 + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. + + Please contact us for any questions: https://www.opencap.ai/#contact + + This example shows how to run a kinematic analysis of gait data. It works + with either treadmill or overground gait. You can compute scalar metrics + as well as gait cycle-averaged kinematic curves. + +''' + +import os +import sys +sys.path.append("../") +sys.path.append("../ActivityAnalyses") + +from gait_analysis import gait_analysis +import utils + + +# %% User-defined variables + +# overground trial +session_id = 'b39b10d1-17c7-4976-b06c-a6aaf33fead2' +trial_name = 'gait_3' + +# treadmill trial 1.25m/s +# session_id = '4d5c3eb1-1a59-4ea1-9178-d3634610561c' +# trial_name = 'walk_1_25ms' + +scalar_names = {'gait_speed','stride_length','step_width','cadence', + 'single_support_time','double_support_time'} + +# how many gait cycles you'd like to analyze +# -1 for all gait cycles detected in the trial +n_gait_cycles = -1 + +# Lowpass filter frequency for kinematics data +filter_frequency = 6 + +# %% Gait analysis + +trial_id = utils.get_trial_id(session_id,trial_name) + + + +session_id = utils.get_trial_json(trial_id)['session'] + +# Local data dir -> will be deleted with lambda instance +sessionDir = os.path.join(os.path.abspath('../Data'),session_id) + +# download data +trialName = utils.download_trial(trial_id,sessionDir,session_id=session_id) + +# init gait analysis +gait_r = gait_analysis(sessionDir, trialName, leg='r', + lowpass_cutoff_frequency_for_coordinate_values=filter_frequency, + n_gait_cycles=n_gait_cycles) +gait_l = gait_analysis(sessionDir, trialName, leg='l', + lowpass_cutoff_frequency_for_coordinate_values=filter_frequency, + n_gait_cycles=n_gait_cycles) + +# compute scalars and get time-normalized kinematic curves +gaitResults = {} +gaitResults['scalars_r'] = gait_r.compute_scalars(scalar_names) +gaitResults['curves_r'] = gait_r.get_coordinates_normalized_time() +gaitResults['scalars_l'] = gait_l.compute_scalars(scalar_names) +gaitResults['curves_l'] = gait_l.get_coordinates_normalized_time() + + +# %% Print scalar results + +print('\nRight foot gait metrics:') +print('(units: m and s)') +print('-----------------') +for key, value in gaitResults['scalars_r'].items(): + rounded_value = round(value, 2) + print(f"{key}: {rounded_value}") + +print('\nLeft foot gait metrics:') +print('(units: m and s)') +print('-----------------') +for key, value in gaitResults['scalars_l'].items(): + rounded_value = round(value, 2) + print(f"{key}: {rounded_value}") + +# %% Plot kinematic curves + +for leg in ['r','l']: + utils.plot_subplots_with_shading(gaitResults['curves_' + leg]['mean'], + gaitResults['curves_' + leg]['sd'], columns=None, + leg=leg) + +test = 1 \ No newline at end of file diff --git a/utils.py b/utils.py index 3393c905..175d4cc0 100644 --- a/utils.py +++ b/utils.py @@ -29,6 +29,7 @@ import glob import zipfile import platform +import matplotlib.pyplot as plt from utilsAPI import get_api_url from utilsAuthentication import get_token @@ -630,4 +631,61 @@ def zipdir(path, ziph): if writeToDB: post_file_to_trial(session_zip,dynamic_ids[-1],tag='session_zip', device_id='all') + + + +def plot_subplots_with_shading(mean_df, sd_df, columns=None, leg=None): + if columns is None: + columns = [col for col in mean_df.columns if col != 'time'] + + columns = [col for col in columns if not any(sub in col for sub in ['_beta', 'mtp', 'time'])] + + if leg == 'r': + columns = [col for col in columns if not col.endswith('_l')] + elif leg == 'l': + columns = [col for col in columns if not col.endswith('_r')] + + num_columns = len(columns) + num_rows = (num_columns + 3) // 4 # Always 4 columns per row + + fig, axes = plt.subplots(num_rows, 4, figsize=(12, 8)) + axes = axes.flatten() + + x_values = np.array([0, 50, 100]) + x_labels = ['0%', '50%', '100%'] + + for i, column in enumerate(columns): + row = i // 4 + ax = axes[i] + + mean_values = mean_df[column] + + ax.plot(mean_values, color='black', label='Mean') + # Check if sd_df is not None before plotting + if sd_df is not None: + sd_values = sd_df[column] + ax.fill_between( + range(len(mean_values)), + mean_values - sd_values, + mean_values + sd_values, + color='gray', + alpha=0.5, + label='Mean ± SD' + ) + + ax.set_xticks(x_values) + ax.set_xticklabels(x_labels) + ax.set_xlabel('% gait cycle' if row == num_rows - 1 else None, fontsize=16) + ax.set_ylabel(column, fontsize=16) + + # Increase font size for axis labels + ax.tick_params(axis='both', which='major', labelsize=12) + + # Remove any unused subplots + if num_columns < num_rows * 4: + for i in range(num_columns, num_rows * 4): + fig.delaxes(axes[i]) + + plt.tight_layout() + plt.show() \ No newline at end of file diff --git a/utilsProcessing.py b/utilsProcessing.py index 6b09ea3d..b4d82946 100644 --- a/utilsProcessing.py +++ b/utilsProcessing.py @@ -19,7 +19,7 @@ ''' import sys -sys.path.append('AnalysisFunctions/ActivityClasses/') +sys.path.append('AnalysisFunctions/') import os import logging @@ -41,11 +41,12 @@ def lowPassFilter(time, data, lowpass_cutoff_frequency, order=4): # %% Segment gait def segment_gait(session_id, trial_name, data_folder, gait_cycles_from_end=0): - from gaitAnalysis import gait_analysis + from gait_analysis import gait_analysis trial_id = get_trial_id(session_id,trial_name) download_trial(trial_id,os.path.join(data_folder,session_id),session_id=session_id) - gait = gait_analysis(os.path.join(data_folder,session_id), trial_name,n_gait_cycles=4) + gait = gait_analysis(os.path.join(data_folder,session_id), trial_name, + n_gait_cycles=-1) heelstrikeTimes = gait.gaitEvents['ipsilateralTime'][gait_cycles_from_end,(0,2)].tolist() return heelstrikeTimes, gait From c78fe08bfbc59c08c9f6b01bbcd913b84643a5d9 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Thu, 31 Aug 2023 15:42:36 -0700 Subject: [PATCH 17/29] clean --- Examples/example_gait_analysis.py | 17 ++++++----------- 1 file changed, 6 insertions(+), 11 deletions(-) diff --git a/Examples/example_gait_analysis.py b/Examples/example_gait_analysis.py index 916e76f9..8c26a22d 100644 --- a/Examples/example_gait_analysis.py +++ b/Examples/example_gait_analysis.py @@ -35,12 +35,12 @@ # %% User-defined variables # overground trial -session_id = 'b39b10d1-17c7-4976-b06c-a6aaf33fead2' -trial_name = 'gait_3' +# session_id = 'b39b10d1-17c7-4976-b06c-a6aaf33fead2' +# trial_name = 'gait_3' # treadmill trial 1.25m/s -# session_id = '4d5c3eb1-1a59-4ea1-9178-d3634610561c' -# trial_name = 'walk_1_25ms' +session_id = '4d5c3eb1-1a59-4ea1-9178-d3634610561c' +trial_name = 'walk_1_25ms' scalar_names = {'gait_speed','stride_length','step_width','cadence', 'single_support_time','double_support_time'} @@ -54,12 +54,9 @@ # %% Gait analysis +# Get trial id from name trial_id = utils.get_trial_id(session_id,trial_name) - - -session_id = utils.get_trial_json(trial_id)['session'] - # Local data dir -> will be deleted with lambda instance sessionDir = os.path.join(os.path.abspath('../Data'),session_id) @@ -103,6 +100,4 @@ for leg in ['r','l']: utils.plot_subplots_with_shading(gaitResults['curves_' + leg]['mean'], gaitResults['curves_' + leg]['sd'], columns=None, - leg=leg) - -test = 1 \ No newline at end of file + leg=leg) \ No newline at end of file From 77b657bb114bc86a81669ab8c8019d806df4576a Mon Sep 17 00:00:00 2001 From: Antoine Falisse Date: Thu, 31 Aug 2023 16:17:26 -0700 Subject: [PATCH 18/29] cleaning --- ActivityAnalyses/gait_analysis.py | 215 ++++++++++++++++-------------- 1 file changed, 116 insertions(+), 99 deletions(-) diff --git a/ActivityAnalyses/gait_analysis.py b/ActivityAnalyses/gait_analysis.py index 6673b862..53fa17fd 100644 --- a/ActivityAnalyses/gait_analysis.py +++ b/ActivityAnalyses/gait_analysis.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ --------------------------------------------------------------------------- OpenCap processing: gaitAnalysis.py @@ -36,51 +35,53 @@ class gait_analysis(kinematics): def __init__(self, sessionDir, trialName, leg='auto', lowpass_cutoff_frequency_for_coordinate_values=-1, - n_gait_cycles=1): + n_gait_cycles=-1): - # inherit init from kinematics class - super().__init__(sessionDir, trialName, - lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) + # Inherit init from kinematics class. + super().__init__( + sessionDir, + trialName, + lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) - # Marker data load and filter - trcFilePath = os.path.join(sessionDir,'MarkerData', - '{}.trc'.format(trialName)) + # Marker data load and filter. + trcFilePath = os.path.join(sessionDir, + 'MarkerData', + '{}.trc'.format(trialName)) self.markerDict = trc_2_dict(trcFilePath) if lowpass_cutoff_frequency_for_coordinate_values > 0: self.markerDict['markers'] = { - marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency_for_coordinate_values) \ - for marker_name, data in self.markerDict['markers'].items()} + marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency_for_coordinate_values) + for marker_name, data in self.markerDict['markers'].items()} - # Coordinate values + # Coordinate values. self.coordinateValues = self.get_coordinate_values() - # Segment gait cycles + # Segment gait cycles. self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles,leg=leg) self.nGaitCycles = np.shape(self.gaitEvents['ipsilateralIdx'])[0] - # determine treadmill speed (0 if overground) + # Determine treadmill speed (0 if overground). self.treadmillSpeed = self.compute_treadmill_speed() - # initialize variables to be lazy loaded + # Initialize variables to be lazy loaded. self._comValues = None self._R_world_to_gait = None - # # Lazy loaded variables (setting state trajectory in utilsKinematics is slow) - # Compute COM trajectory and gait frame...used in multiple scalar computations + # Compute COM trajectory. def comValues(self): if self._comValues is None: self._comValues = self.get_center_of_mass_values() return self._comValues + # Compute gait frame. def R_world_to_gait(self): if self._R_world_to_gait is None: self._R_world_to_gait = self.compute_gait_frame() return self._R_world_to_gait - # # End Lazy loading def compute_scalars(self,scalarNames): - # verify that scalarNames are methods in gait_analysis + # Verify that scalarNames are methods in gait_analysis. method_names = [func for func in dir(self) if callable(getattr(self, func))] possibleMethods = [entry for entry in method_names if 'compute_' in entry] @@ -92,7 +93,7 @@ def compute_scalars(self,scalarNames): nonexistant_methods = [entry for entry in scalarNames if 'compute_' + entry not in method_names] if len(nonexistant_methods) > 0: - raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') + raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' does not exist in gait_analysis class.') scalarDict = {} for scalarName in scalarNames: @@ -107,13 +108,15 @@ def compute_stride_length(self): calc_position = self.markerDict['markers'][leg + '_calc_study'] - # find stride length on treadmill - # difference in ipsilateral calcaneus position at heel strike + treadmill speed * time - strideLength = np.linalg.norm(calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ - calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + \ - self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) - - # average across all strides + # On treadmill, the stride length is the difference in ipsilateral + # calcaneus position at heel strike + treadmill speed * time. + strideLength = ( + np.linalg.norm( + calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - + calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + + self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)])) + + # Average across all strides. strideLength = np.mean(strideLength) return strideLength @@ -121,26 +124,28 @@ def compute_stride_length(self): def compute_gait_speed(self): comValuesArray = np.vstack((self.comValues()['x'],self.comValues()['y'],self.comValues()['z'])).T - gait_speed = (np.linalg.norm(comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ - comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2)) / \ - np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed + gait_speed = ( + np.linalg.norm( + comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - + comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) / + np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed) - # average across all strides + # Average across all strides. gait_speed = np.mean(gait_speed) return gait_speed def compute_cadence(self): - # in steps per second + # In steps per second. cadence = 1/np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) - # average across strides + # Average across all strides. cadence = np.mean(cadence) return cadence - def compute_treadmill_speed(self): + def compute_treadmill_speed(self, overground_speed_threshold=0.3): leg,_ = self.get_leg() @@ -150,7 +155,7 @@ def compute_treadmill_speed(self): startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.1*stanceTimeLength).astype(int) endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceTimeLength).astype(int) - # average instantaneous velocities + # Average instantaneous velocities. dt = np.diff(self.markerDict['time'][:2])[0] for i in range(self.nGaitCycles): footVel = np.linalg.norm(np.mean(np.diff( @@ -158,31 +163,35 @@ def compute_treadmill_speed(self): treadmillSpeed = np.mean(footVel) - # overground - if treadmillSpeed < .3: + # Overground. + if treadmillSpeed < overground_speed_threshold: treadmillSpeed = 0 return treadmillSpeed def compute_step_width(self): - # get ankle joint center positions - leg,contLeg = self.get_leg() - ankle_position_ips = (self.markerDict['markers'][leg + '_ankle_study'] + - self.markerDict['markers'][leg + '_mankle_study'])/2 - ankle_position_cont = (self.markerDict['markers'][contLeg + '_ankle_study'] + - self.markerDict['markers'][contLeg + '_mankle_study'])/2 + leg,contLeg = self.get_leg() - ankleVector = ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - \ - ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]] + # Get ankle joint center positions. + ankle_position_ips = ( + self.markerDict['markers'][leg + '_ankle_study'] + + self.markerDict['markers'][leg + '_mankle_study'])/2 + ankle_position_cont = ( + self.markerDict['markers'][contLeg + '_ankle_study'] + + self.markerDict['markers'][contLeg + '_mankle_study'])/2 + ankleVector = ( + ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - + ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]]) - ankleVector_inGaitFrame = np.array([np.dot(ankleVector[i,:], self.R_world_to_gait()[i,:,:]) \ - for i in range(self.nGaitCycles)]) + ankleVector_inGaitFrame = np.array( + [np.dot(ankleVector[i,:], self.R_world_to_gait()[i,:,:]) + for i in range(self.nGaitCycles)]) - # step width is z distance + # Step width is z distance. stepWidth = np.abs(ankleVector_inGaitFrame[:,2]) - # average across gait cycles + # Average across all strides. stepWidth = np.mean(stepWidth) return stepWidth @@ -191,43 +200,45 @@ def compute_single_support_time(self): singleSupportTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - # average across strides + # Average across all strides. singleSupportTime = np.mean(singleSupportTime) return singleSupportTime def compute_double_support_time(self): - # ipsilateral single support time - contralateral swing time - doubleSupportTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - \ - np.diff(self.gaitEvents['contralateralTime'][:,:2]) + # Ipsilateral single support time - contralateral swing time. + doubleSupportTime = ( + np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - + np.diff(self.gaitEvents['contralateralTime'][:,:2])) - # average across strides + # Average across all strides. doubleSupportTime = np.mean(doubleSupportTime) return doubleSupportTime def compute_gait_frame(self): - # Create frame for each gait cycle with x: pelvis heading, - # z: average vector between ASIS during gait cycle, y: cross. + + # Create frame for each gait cycle with x: pelvis heading, + # z: average vector between ASIS during gait cycle, y: cross. - # Pelvis center trajectory (for overground heading vector) + # Pelvis center trajectory (for overground heading vector). pelvisMarkerNames = ['r.ASIS_study','L.ASIS_study','r.PSIS_study','L.PSIS_study'] pelvisMarkers = [self.markerDict['markers'][mkr] for mkr in pelvisMarkerNames] pelvisCenter = np.mean(np.array(pelvisMarkers),axis=0) - # ankle trajectory (for treadmill heading vector) + # Ankle trajectory (for treadmill heading vector). leg = self.gaitEvents['ipsilateralLeg'] if leg == 'l': leg='L' anklePos = self.markerDict['markers'][leg + '_ankle_study'] - # vector from left ASIS to right ASIS (for mediolateral direction) + # Vector from left ASIS to right ASIS (for mediolateral direction). asisMarkerNames = ['L.ASIS_study','r.ASIS_study'] asisMarkers = [self.markerDict['markers'][mkr] for mkr in asisMarkerNames] asisVector = np.squeeze(np.diff(np.array(asisMarkers),axis=0)) - # heading vector per gait cycle - # if overground, use pelvis center trajectory; treadmill: ankle trajectory + # Heading vector per gait cycle. + # If overground, use pelvis center trajectory; treadmill: ankle trajectory. if self.treadmillSpeed == 0: x = np.diff(pelvisCenter[self.gaitEvents['ipsilateralIdx'][:,(0,2)],:],axis=1)[:,0,:] x = x / np.linalg.norm(x,axis=1,keepdims=True) @@ -238,22 +249,23 @@ def compute_gait_frame(self): anklePos[self.gaitEvents['ipsilateralIdx'][i,1]] x = x / np.linalg.norm(x,axis=1,keepdims=True) - # mean ASIS vector over gait cycle + # Mean ASIS vector over gait cycle. z = np.zeros((self.nGaitCycles,3)) for i in range(self.nGaitCycles): z[i,:] = np.mean(asisVector[self.gaitEvents['ipsilateralIdx'][i,0]: \ self.gaitEvents['ipsilateralIdx'][i,2]],axis=0) z = z / np.linalg.norm(z,axis=1,keepdims=True) - # cross to get y + # Cross to get y. y = np.cross(z,x) - # 3x3xnSteps + # 3x3xnSteps. R_lab_to_gait = np.stack((x.T,y.T,z.T),axis=1).transpose((2, 0, 1)) return R_lab_to_gait def get_leg(self): + if self.gaitEvents['ipsilateralLeg'] == 'r': leg = 'r' contLeg = 'L' @@ -275,44 +287,49 @@ def get_coordinates_normalized_time(self): for i in range(coordValues.shape[1])],axis=1)) coordinateValuesTimeNormalized = {} - # average + # Average. coordVals_mean = np.mean(np.array(coordValuesNorm),axis=0) coordinateValuesTimeNormalized['mean'] = pd.DataFrame(data=coordVals_mean, columns=colNames) - # standard deviation + # Standard deviation. if self.nGaitCycles >2: coordVals_sd = np.std(np.array(coordValuesNorm), axis=0) coordinateValuesTimeNormalized['sd'] = pd.DataFrame(data=coordVals_sd, columns=colNames) else: coordinateValuesTimeNormalized['sd'] = None - #return to dataframe + # Return to dataframe. coordinateValuesTimeNormalized['indiv'] = [pd.DataFrame(data=d, columns=colNames) for d in coordValuesNorm] return coordinateValuesTimeNormalized def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): - # n_gait_cycles = -1 finds all accessible gait cycles. Otherwise, it finds - # that many gait cycles, working backwards from end of trial. - - # subtract sacrum from foot - # visually, it looks like the position-based approach will be more robust - r_calc_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ - 'markers']['r.PSIS_study'])[:,0] - r_toe_rel_x = (self.markerDict['markers']['r_toe_study'] - self.markerDict[ - 'markers']['r.PSIS_study'])[:,0] - # repeat for left - l_calc_rel_x = (self.markerDict['markers']['L_calc_study'] - self.markerDict[ - 'markers']['L.PSIS_study'])[:,0] - l_toe_rel_x = (self.markerDict['markers']['L_toe_study'] - self.markerDict[ - 'markers']['L.PSIS_study'])[:,0] - - # Find HS + # n_gait_cycles = -1 finds all accessible gait cycles. Otherwise, it + # finds that many gait cycles, working backwards from end of trial. + + # Subtract sacrum from foot. + # It looks like the position-based approach will be more robust. + r_calc_rel_x = ( + self.markerDict['markers']['r_calc_study'] - + self.markerDict['markers']['r.PSIS_study'])[:,0] + r_toe_rel_x = ( + self.markerDict['markers']['r_toe_study'] - + self.markerDict['markers']['r.PSIS_study'])[:,0] + + # Repeat for left. + l_calc_rel_x = ( + self.markerDict['markers']['L_calc_study'] - + self.markerDict['markers']['L.PSIS_study'])[:,0] + l_toe_rel_x = ( + self.markerDict['markers']['L_toe_study'] - + self.markerDict['markers']['L.PSIS_study'])[:,0] + + # Find HS. rHS, _ = find_peaks(r_calc_rel_x, prominence=0.3) lHS, _ = find_peaks(l_calc_rel_x, prominence=0.3) - # Find TO + # Find TO. rTO, _ = find_peaks(-r_toe_rel_x, prominence=0.3) lTO, _ = find_peaks(-l_toe_rel_x, prominence=0.3) @@ -333,15 +350,15 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): plt.scatter(self.markerDict['time'][lTO], l_toe_rel_x[lTO], color='blue', label='lTO') plt.legend() - # find the number of gait cycles for the foot of interest + # Find the number of gait cycles for the foot of interest. if leg=='auto': - # find the last HS of either foot + # Find the last HS of either foot. if rHS[-1] > lHS[-1]: leg = 'r' else: leg = 'l' - # find the number of gait cycles for the foot of interest + # Find the number of gait cycles for the foot of interest. if leg == 'r': hsIps = rHS toIps = rTO @@ -354,34 +371,36 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): toCont = rTO if len(hsIps)-1 < n_gait_cycles: - print('You requested {} gait cycles, but only {} were found. Proceeding with this number.'.format( - n_gait_cycles,len(hsIps)-1)) + print('You requested {} gait cycles, but only {} were found. ' + 'Proceeding with this number.'.format(n_gait_cycles,len(hsIps)-1)) n_gait_cycles = len(hsIps)-1 if n_gait_cycles == -1: n_gait_cycles = len(hsIps)-1 print('Processing {} gait cycles.'.format(n_gait_cycles)) - # Ipsilateral gait events: heel strike, toe-off, heel strike + # Ipsilateral gait events: heel strike, toe-off, heel strike. gaitEvents_ips = np.zeros((n_gait_cycles, 3),dtype=np.int) - # Contralateral gait events: toe-off, heel strike + # Contralateral gait events: toe-off, heel strike. gaitEvents_cont = np.zeros((n_gait_cycles, 2),dtype=np.int) if n_gait_cycles <1: raise Exception('Not enough gait cycles found.') for i in range(n_gait_cycles): - # ipsilateral HS, TO, HS + # Ipsilateral HS, TO, HS. gaitEvents_ips[i,0] = hsIps[-i-2] gaitEvents_ips[i,2] = hsIps[-i-1] - # iterate in reverse through ipsilateral TO, finding the one that is within the range of gaitEvents_ips + # Iterate in reverse through ipsilateral TO, finding the one that + # is within the range of gaitEvents_ips. toIpsFound = False for j in range(len(toIps)): if toIps[-j-1] > gaitEvents_ips[i,0] and toIps[-j-1] < gaitEvents_ips[i,2] and not toIpsFound: gaitEvents_ips[i,1] = toIps[-j-1] toIpsFound = True - # contralateral TO, HS - # iterate in reverse through contralateral HS and TO, finding the one that is within the range of gaitEvents_ips + # Contralateral TO, HS. + # Iterate in reverse through contralateral HS and TO, finding the + # one that is within the range of gaitEvents_ips hsContFound = False toContFound = False for j in range(len(toCont)): @@ -394,13 +413,13 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): gaitEvents_cont[i,1] = hsCont[-j-1] hsContFound = True - # making contralateral gait events optional + # Making contralateral gait events optional. if not toContFound or not hsContFound: raise Warning('Could not find contralateral gait event within ipsilateral gait event range.') gaitEvents_cont[i,0] = np.nan gaitEvents_cont[i,1] = np.nan - # convert gaitEvents to times using self.markerDict['time'] + # Convert gaitEvents to times using self.markerDict['time']. gaitEventTimes_ips = self.markerDict['time'][gaitEvents_ips] gaitEventTimes_cont = self.markerDict['time'][gaitEvents_cont] @@ -412,7 +431,5 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): 'eventNamesContralateral':['TO','HS'], 'ipsilateralLeg':leg} - return gaitEvents - - # Lazy loading some things that are computationally expensive + return gaitEvents \ No newline at end of file From cd8371e2fb139c457bad00672db07c71ae45d5b7 Mon Sep 17 00:00:00 2001 From: Antoine Falisse Date: Thu, 31 Aug 2023 16:19:16 -0700 Subject: [PATCH 19/29] revert changes made by accident --- ActivityAnalyses/gait_analysis.py | 215 ++++++++++++++---------------- 1 file changed, 99 insertions(+), 116 deletions(-) diff --git a/ActivityAnalyses/gait_analysis.py b/ActivityAnalyses/gait_analysis.py index 53fa17fd..6673b862 100644 --- a/ActivityAnalyses/gait_analysis.py +++ b/ActivityAnalyses/gait_analysis.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ --------------------------------------------------------------------------- OpenCap processing: gaitAnalysis.py @@ -35,53 +36,51 @@ class gait_analysis(kinematics): def __init__(self, sessionDir, trialName, leg='auto', lowpass_cutoff_frequency_for_coordinate_values=-1, - n_gait_cycles=-1): + n_gait_cycles=1): - # Inherit init from kinematics class. - super().__init__( - sessionDir, - trialName, - lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) + # inherit init from kinematics class + super().__init__(sessionDir, trialName, + lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) - # Marker data load and filter. - trcFilePath = os.path.join(sessionDir, - 'MarkerData', - '{}.trc'.format(trialName)) + # Marker data load and filter + trcFilePath = os.path.join(sessionDir,'MarkerData', + '{}.trc'.format(trialName)) self.markerDict = trc_2_dict(trcFilePath) if lowpass_cutoff_frequency_for_coordinate_values > 0: self.markerDict['markers'] = { - marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency_for_coordinate_values) - for marker_name, data in self.markerDict['markers'].items()} + marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency_for_coordinate_values) \ + for marker_name, data in self.markerDict['markers'].items()} - # Coordinate values. + # Coordinate values self.coordinateValues = self.get_coordinate_values() - # Segment gait cycles. + # Segment gait cycles self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles,leg=leg) self.nGaitCycles = np.shape(self.gaitEvents['ipsilateralIdx'])[0] - # Determine treadmill speed (0 if overground). + # determine treadmill speed (0 if overground) self.treadmillSpeed = self.compute_treadmill_speed() - # Initialize variables to be lazy loaded. + # initialize variables to be lazy loaded self._comValues = None self._R_world_to_gait = None - # Compute COM trajectory. + # # Lazy loaded variables (setting state trajectory in utilsKinematics is slow) + # Compute COM trajectory and gait frame...used in multiple scalar computations def comValues(self): if self._comValues is None: self._comValues = self.get_center_of_mass_values() return self._comValues - # Compute gait frame. def R_world_to_gait(self): if self._R_world_to_gait is None: self._R_world_to_gait = self.compute_gait_frame() return self._R_world_to_gait + # # End Lazy loading def compute_scalars(self,scalarNames): - # Verify that scalarNames are methods in gait_analysis. + # verify that scalarNames are methods in gait_analysis method_names = [func for func in dir(self) if callable(getattr(self, func))] possibleMethods = [entry for entry in method_names if 'compute_' in entry] @@ -93,7 +92,7 @@ def compute_scalars(self,scalarNames): nonexistant_methods = [entry for entry in scalarNames if 'compute_' + entry not in method_names] if len(nonexistant_methods) > 0: - raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' does not exist in gait_analysis class.') + raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') scalarDict = {} for scalarName in scalarNames: @@ -108,15 +107,13 @@ def compute_stride_length(self): calc_position = self.markerDict['markers'][leg + '_calc_study'] - # On treadmill, the stride length is the difference in ipsilateral - # calcaneus position at heel strike + treadmill speed * time. - strideLength = ( - np.linalg.norm( - calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - - calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + - self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)])) - - # Average across all strides. + # find stride length on treadmill + # difference in ipsilateral calcaneus position at heel strike + treadmill speed * time + strideLength = np.linalg.norm(calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ + calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + \ + self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + + # average across all strides strideLength = np.mean(strideLength) return strideLength @@ -124,28 +121,26 @@ def compute_stride_length(self): def compute_gait_speed(self): comValuesArray = np.vstack((self.comValues()['x'],self.comValues()['y'],self.comValues()['z'])).T - gait_speed = ( - np.linalg.norm( - comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - - comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) / - np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed) + gait_speed = (np.linalg.norm(comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ + comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2)) / \ + np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed - # Average across all strides. + # average across all strides gait_speed = np.mean(gait_speed) return gait_speed def compute_cadence(self): - # In steps per second. + # in steps per second cadence = 1/np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) - # Average across all strides. + # average across strides cadence = np.mean(cadence) return cadence - def compute_treadmill_speed(self, overground_speed_threshold=0.3): + def compute_treadmill_speed(self): leg,_ = self.get_leg() @@ -155,7 +150,7 @@ def compute_treadmill_speed(self, overground_speed_threshold=0.3): startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.1*stanceTimeLength).astype(int) endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceTimeLength).astype(int) - # Average instantaneous velocities. + # average instantaneous velocities dt = np.diff(self.markerDict['time'][:2])[0] for i in range(self.nGaitCycles): footVel = np.linalg.norm(np.mean(np.diff( @@ -163,35 +158,31 @@ def compute_treadmill_speed(self, overground_speed_threshold=0.3): treadmillSpeed = np.mean(footVel) - # Overground. - if treadmillSpeed < overground_speed_threshold: + # overground + if treadmillSpeed < .3: treadmillSpeed = 0 return treadmillSpeed def compute_step_width(self): - + # get ankle joint center positions leg,contLeg = self.get_leg() - # Get ankle joint center positions. - ankle_position_ips = ( - self.markerDict['markers'][leg + '_ankle_study'] + - self.markerDict['markers'][leg + '_mankle_study'])/2 - ankle_position_cont = ( - self.markerDict['markers'][contLeg + '_ankle_study'] + - self.markerDict['markers'][contLeg + '_mankle_study'])/2 - ankleVector = ( - ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - - ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]]) + ankle_position_ips = (self.markerDict['markers'][leg + '_ankle_study'] + + self.markerDict['markers'][leg + '_mankle_study'])/2 + ankle_position_cont = (self.markerDict['markers'][contLeg + '_ankle_study'] + + self.markerDict['markers'][contLeg + '_mankle_study'])/2 + + ankleVector = ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - \ + ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]] - ankleVector_inGaitFrame = np.array( - [np.dot(ankleVector[i,:], self.R_world_to_gait()[i,:,:]) - for i in range(self.nGaitCycles)]) + ankleVector_inGaitFrame = np.array([np.dot(ankleVector[i,:], self.R_world_to_gait()[i,:,:]) \ + for i in range(self.nGaitCycles)]) - # Step width is z distance. + # step width is z distance stepWidth = np.abs(ankleVector_inGaitFrame[:,2]) - # Average across all strides. + # average across gait cycles stepWidth = np.mean(stepWidth) return stepWidth @@ -200,45 +191,43 @@ def compute_single_support_time(self): singleSupportTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - # Average across all strides. + # average across strides singleSupportTime = np.mean(singleSupportTime) return singleSupportTime def compute_double_support_time(self): - # Ipsilateral single support time - contralateral swing time. - doubleSupportTime = ( - np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - - np.diff(self.gaitEvents['contralateralTime'][:,:2])) + # ipsilateral single support time - contralateral swing time + doubleSupportTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - \ + np.diff(self.gaitEvents['contralateralTime'][:,:2]) - # Average across all strides. + # average across strides doubleSupportTime = np.mean(doubleSupportTime) return doubleSupportTime def compute_gait_frame(self): - - # Create frame for each gait cycle with x: pelvis heading, - # z: average vector between ASIS during gait cycle, y: cross. + # Create frame for each gait cycle with x: pelvis heading, + # z: average vector between ASIS during gait cycle, y: cross. - # Pelvis center trajectory (for overground heading vector). + # Pelvis center trajectory (for overground heading vector) pelvisMarkerNames = ['r.ASIS_study','L.ASIS_study','r.PSIS_study','L.PSIS_study'] pelvisMarkers = [self.markerDict['markers'][mkr] for mkr in pelvisMarkerNames] pelvisCenter = np.mean(np.array(pelvisMarkers),axis=0) - # Ankle trajectory (for treadmill heading vector). + # ankle trajectory (for treadmill heading vector) leg = self.gaitEvents['ipsilateralLeg'] if leg == 'l': leg='L' anklePos = self.markerDict['markers'][leg + '_ankle_study'] - # Vector from left ASIS to right ASIS (for mediolateral direction). + # vector from left ASIS to right ASIS (for mediolateral direction) asisMarkerNames = ['L.ASIS_study','r.ASIS_study'] asisMarkers = [self.markerDict['markers'][mkr] for mkr in asisMarkerNames] asisVector = np.squeeze(np.diff(np.array(asisMarkers),axis=0)) - # Heading vector per gait cycle. - # If overground, use pelvis center trajectory; treadmill: ankle trajectory. + # heading vector per gait cycle + # if overground, use pelvis center trajectory; treadmill: ankle trajectory if self.treadmillSpeed == 0: x = np.diff(pelvisCenter[self.gaitEvents['ipsilateralIdx'][:,(0,2)],:],axis=1)[:,0,:] x = x / np.linalg.norm(x,axis=1,keepdims=True) @@ -249,23 +238,22 @@ def compute_gait_frame(self): anklePos[self.gaitEvents['ipsilateralIdx'][i,1]] x = x / np.linalg.norm(x,axis=1,keepdims=True) - # Mean ASIS vector over gait cycle. + # mean ASIS vector over gait cycle z = np.zeros((self.nGaitCycles,3)) for i in range(self.nGaitCycles): z[i,:] = np.mean(asisVector[self.gaitEvents['ipsilateralIdx'][i,0]: \ self.gaitEvents['ipsilateralIdx'][i,2]],axis=0) z = z / np.linalg.norm(z,axis=1,keepdims=True) - # Cross to get y. + # cross to get y y = np.cross(z,x) - # 3x3xnSteps. + # 3x3xnSteps R_lab_to_gait = np.stack((x.T,y.T,z.T),axis=1).transpose((2, 0, 1)) return R_lab_to_gait def get_leg(self): - if self.gaitEvents['ipsilateralLeg'] == 'r': leg = 'r' contLeg = 'L' @@ -287,49 +275,44 @@ def get_coordinates_normalized_time(self): for i in range(coordValues.shape[1])],axis=1)) coordinateValuesTimeNormalized = {} - # Average. + # average coordVals_mean = np.mean(np.array(coordValuesNorm),axis=0) coordinateValuesTimeNormalized['mean'] = pd.DataFrame(data=coordVals_mean, columns=colNames) - # Standard deviation. + # standard deviation if self.nGaitCycles >2: coordVals_sd = np.std(np.array(coordValuesNorm), axis=0) coordinateValuesTimeNormalized['sd'] = pd.DataFrame(data=coordVals_sd, columns=colNames) else: coordinateValuesTimeNormalized['sd'] = None - # Return to dataframe. + #return to dataframe coordinateValuesTimeNormalized['indiv'] = [pd.DataFrame(data=d, columns=colNames) for d in coordValuesNorm] return coordinateValuesTimeNormalized def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): + # n_gait_cycles = -1 finds all accessible gait cycles. Otherwise, it finds + # that many gait cycles, working backwards from end of trial. + + # subtract sacrum from foot + # visually, it looks like the position-based approach will be more robust + r_calc_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ + 'markers']['r.PSIS_study'])[:,0] + r_toe_rel_x = (self.markerDict['markers']['r_toe_study'] - self.markerDict[ + 'markers']['r.PSIS_study'])[:,0] - # n_gait_cycles = -1 finds all accessible gait cycles. Otherwise, it - # finds that many gait cycles, working backwards from end of trial. - - # Subtract sacrum from foot. - # It looks like the position-based approach will be more robust. - r_calc_rel_x = ( - self.markerDict['markers']['r_calc_study'] - - self.markerDict['markers']['r.PSIS_study'])[:,0] - r_toe_rel_x = ( - self.markerDict['markers']['r_toe_study'] - - self.markerDict['markers']['r.PSIS_study'])[:,0] - - # Repeat for left. - l_calc_rel_x = ( - self.markerDict['markers']['L_calc_study'] - - self.markerDict['markers']['L.PSIS_study'])[:,0] - l_toe_rel_x = ( - self.markerDict['markers']['L_toe_study'] - - self.markerDict['markers']['L.PSIS_study'])[:,0] - - # Find HS. + # repeat for left + l_calc_rel_x = (self.markerDict['markers']['L_calc_study'] - self.markerDict[ + 'markers']['L.PSIS_study'])[:,0] + l_toe_rel_x = (self.markerDict['markers']['L_toe_study'] - self.markerDict[ + 'markers']['L.PSIS_study'])[:,0] + + # Find HS rHS, _ = find_peaks(r_calc_rel_x, prominence=0.3) lHS, _ = find_peaks(l_calc_rel_x, prominence=0.3) - # Find TO. + # Find TO rTO, _ = find_peaks(-r_toe_rel_x, prominence=0.3) lTO, _ = find_peaks(-l_toe_rel_x, prominence=0.3) @@ -350,15 +333,15 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): plt.scatter(self.markerDict['time'][lTO], l_toe_rel_x[lTO], color='blue', label='lTO') plt.legend() - # Find the number of gait cycles for the foot of interest. + # find the number of gait cycles for the foot of interest if leg=='auto': - # Find the last HS of either foot. + # find the last HS of either foot if rHS[-1] > lHS[-1]: leg = 'r' else: leg = 'l' - # Find the number of gait cycles for the foot of interest. + # find the number of gait cycles for the foot of interest if leg == 'r': hsIps = rHS toIps = rTO @@ -371,36 +354,34 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): toCont = rTO if len(hsIps)-1 < n_gait_cycles: - print('You requested {} gait cycles, but only {} were found. ' - 'Proceeding with this number.'.format(n_gait_cycles,len(hsIps)-1)) + print('You requested {} gait cycles, but only {} were found. Proceeding with this number.'.format( + n_gait_cycles,len(hsIps)-1)) n_gait_cycles = len(hsIps)-1 if n_gait_cycles == -1: n_gait_cycles = len(hsIps)-1 print('Processing {} gait cycles.'.format(n_gait_cycles)) - # Ipsilateral gait events: heel strike, toe-off, heel strike. + # Ipsilateral gait events: heel strike, toe-off, heel strike gaitEvents_ips = np.zeros((n_gait_cycles, 3),dtype=np.int) - # Contralateral gait events: toe-off, heel strike. + # Contralateral gait events: toe-off, heel strike gaitEvents_cont = np.zeros((n_gait_cycles, 2),dtype=np.int) if n_gait_cycles <1: raise Exception('Not enough gait cycles found.') for i in range(n_gait_cycles): - # Ipsilateral HS, TO, HS. + # ipsilateral HS, TO, HS gaitEvents_ips[i,0] = hsIps[-i-2] gaitEvents_ips[i,2] = hsIps[-i-1] - # Iterate in reverse through ipsilateral TO, finding the one that - # is within the range of gaitEvents_ips. + # iterate in reverse through ipsilateral TO, finding the one that is within the range of gaitEvents_ips toIpsFound = False for j in range(len(toIps)): if toIps[-j-1] > gaitEvents_ips[i,0] and toIps[-j-1] < gaitEvents_ips[i,2] and not toIpsFound: gaitEvents_ips[i,1] = toIps[-j-1] toIpsFound = True - # Contralateral TO, HS. - # Iterate in reverse through contralateral HS and TO, finding the - # one that is within the range of gaitEvents_ips + # contralateral TO, HS + # iterate in reverse through contralateral HS and TO, finding the one that is within the range of gaitEvents_ips hsContFound = False toContFound = False for j in range(len(toCont)): @@ -413,13 +394,13 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): gaitEvents_cont[i,1] = hsCont[-j-1] hsContFound = True - # Making contralateral gait events optional. + # making contralateral gait events optional if not toContFound or not hsContFound: raise Warning('Could not find contralateral gait event within ipsilateral gait event range.') gaitEvents_cont[i,0] = np.nan gaitEvents_cont[i,1] = np.nan - # Convert gaitEvents to times using self.markerDict['time']. + # convert gaitEvents to times using self.markerDict['time'] gaitEventTimes_ips = self.markerDict['time'][gaitEvents_ips] gaitEventTimes_cont = self.markerDict['time'][gaitEvents_cont] @@ -431,5 +412,7 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): 'eventNamesContralateral':['TO','HS'], 'ipsilateralLeg':leg} - return gaitEvents + return gaitEvents + + # Lazy loading some things that are computationally expensive \ No newline at end of file From bfe33984dfc62a4b4f04b9921dbfccf2d8db8ca5 Mon Sep 17 00:00:00 2001 From: Antoine Falisse Date: Thu, 31 Aug 2023 16:20:18 -0700 Subject: [PATCH 20/29] cleaning --- ActivityAnalyses/gait_analysis.py | 215 ++++++++++++++++-------------- 1 file changed, 116 insertions(+), 99 deletions(-) diff --git a/ActivityAnalyses/gait_analysis.py b/ActivityAnalyses/gait_analysis.py index 6673b862..53fa17fd 100644 --- a/ActivityAnalyses/gait_analysis.py +++ b/ActivityAnalyses/gait_analysis.py @@ -1,4 +1,3 @@ -# -*- coding: utf-8 -*- """ --------------------------------------------------------------------------- OpenCap processing: gaitAnalysis.py @@ -36,51 +35,53 @@ class gait_analysis(kinematics): def __init__(self, sessionDir, trialName, leg='auto', lowpass_cutoff_frequency_for_coordinate_values=-1, - n_gait_cycles=1): + n_gait_cycles=-1): - # inherit init from kinematics class - super().__init__(sessionDir, trialName, - lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) + # Inherit init from kinematics class. + super().__init__( + sessionDir, + trialName, + lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) - # Marker data load and filter - trcFilePath = os.path.join(sessionDir,'MarkerData', - '{}.trc'.format(trialName)) + # Marker data load and filter. + trcFilePath = os.path.join(sessionDir, + 'MarkerData', + '{}.trc'.format(trialName)) self.markerDict = trc_2_dict(trcFilePath) if lowpass_cutoff_frequency_for_coordinate_values > 0: self.markerDict['markers'] = { - marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency_for_coordinate_values) \ - for marker_name, data in self.markerDict['markers'].items()} + marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency_for_coordinate_values) + for marker_name, data in self.markerDict['markers'].items()} - # Coordinate values + # Coordinate values. self.coordinateValues = self.get_coordinate_values() - # Segment gait cycles + # Segment gait cycles. self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles,leg=leg) self.nGaitCycles = np.shape(self.gaitEvents['ipsilateralIdx'])[0] - # determine treadmill speed (0 if overground) + # Determine treadmill speed (0 if overground). self.treadmillSpeed = self.compute_treadmill_speed() - # initialize variables to be lazy loaded + # Initialize variables to be lazy loaded. self._comValues = None self._R_world_to_gait = None - # # Lazy loaded variables (setting state trajectory in utilsKinematics is slow) - # Compute COM trajectory and gait frame...used in multiple scalar computations + # Compute COM trajectory. def comValues(self): if self._comValues is None: self._comValues = self.get_center_of_mass_values() return self._comValues + # Compute gait frame. def R_world_to_gait(self): if self._R_world_to_gait is None: self._R_world_to_gait = self.compute_gait_frame() return self._R_world_to_gait - # # End Lazy loading def compute_scalars(self,scalarNames): - # verify that scalarNames are methods in gait_analysis + # Verify that scalarNames are methods in gait_analysis. method_names = [func for func in dir(self) if callable(getattr(self, func))] possibleMethods = [entry for entry in method_names if 'compute_' in entry] @@ -92,7 +93,7 @@ def compute_scalars(self,scalarNames): nonexistant_methods = [entry for entry in scalarNames if 'compute_' + entry not in method_names] if len(nonexistant_methods) > 0: - raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' not in gait_analysis class.') + raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' does not exist in gait_analysis class.') scalarDict = {} for scalarName in scalarNames: @@ -107,13 +108,15 @@ def compute_stride_length(self): calc_position = self.markerDict['markers'][leg + '_calc_study'] - # find stride length on treadmill - # difference in ipsilateral calcaneus position at heel strike + treadmill speed * time - strideLength = np.linalg.norm(calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ - calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + \ - self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) - - # average across all strides + # On treadmill, the stride length is the difference in ipsilateral + # calcaneus position at heel strike + treadmill speed * time. + strideLength = ( + np.linalg.norm( + calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - + calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + + self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)])) + + # Average across all strides. strideLength = np.mean(strideLength) return strideLength @@ -121,26 +124,28 @@ def compute_stride_length(self): def compute_gait_speed(self): comValuesArray = np.vstack((self.comValues()['x'],self.comValues()['y'],self.comValues()['z'])).T - gait_speed = (np.linalg.norm(comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - \ - comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2)) / \ - np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed + gait_speed = ( + np.linalg.norm( + comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - + comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) / + np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed) - # average across all strides + # Average across all strides. gait_speed = np.mean(gait_speed) return gait_speed def compute_cadence(self): - # in steps per second + # In steps per second. cadence = 1/np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) - # average across strides + # Average across all strides. cadence = np.mean(cadence) return cadence - def compute_treadmill_speed(self): + def compute_treadmill_speed(self, overground_speed_threshold=0.3): leg,_ = self.get_leg() @@ -150,7 +155,7 @@ def compute_treadmill_speed(self): startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.1*stanceTimeLength).astype(int) endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceTimeLength).astype(int) - # average instantaneous velocities + # Average instantaneous velocities. dt = np.diff(self.markerDict['time'][:2])[0] for i in range(self.nGaitCycles): footVel = np.linalg.norm(np.mean(np.diff( @@ -158,31 +163,35 @@ def compute_treadmill_speed(self): treadmillSpeed = np.mean(footVel) - # overground - if treadmillSpeed < .3: + # Overground. + if treadmillSpeed < overground_speed_threshold: treadmillSpeed = 0 return treadmillSpeed def compute_step_width(self): - # get ankle joint center positions - leg,contLeg = self.get_leg() - ankle_position_ips = (self.markerDict['markers'][leg + '_ankle_study'] + - self.markerDict['markers'][leg + '_mankle_study'])/2 - ankle_position_cont = (self.markerDict['markers'][contLeg + '_ankle_study'] + - self.markerDict['markers'][contLeg + '_mankle_study'])/2 + leg,contLeg = self.get_leg() - ankleVector = ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - \ - ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]] + # Get ankle joint center positions. + ankle_position_ips = ( + self.markerDict['markers'][leg + '_ankle_study'] + + self.markerDict['markers'][leg + '_mankle_study'])/2 + ankle_position_cont = ( + self.markerDict['markers'][contLeg + '_ankle_study'] + + self.markerDict['markers'][contLeg + '_mankle_study'])/2 + ankleVector = ( + ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - + ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]]) - ankleVector_inGaitFrame = np.array([np.dot(ankleVector[i,:], self.R_world_to_gait()[i,:,:]) \ - for i in range(self.nGaitCycles)]) + ankleVector_inGaitFrame = np.array( + [np.dot(ankleVector[i,:], self.R_world_to_gait()[i,:,:]) + for i in range(self.nGaitCycles)]) - # step width is z distance + # Step width is z distance. stepWidth = np.abs(ankleVector_inGaitFrame[:,2]) - # average across gait cycles + # Average across all strides. stepWidth = np.mean(stepWidth) return stepWidth @@ -191,43 +200,45 @@ def compute_single_support_time(self): singleSupportTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - # average across strides + # Average across all strides. singleSupportTime = np.mean(singleSupportTime) return singleSupportTime def compute_double_support_time(self): - # ipsilateral single support time - contralateral swing time - doubleSupportTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - \ - np.diff(self.gaitEvents['contralateralTime'][:,:2]) + # Ipsilateral single support time - contralateral swing time. + doubleSupportTime = ( + np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - + np.diff(self.gaitEvents['contralateralTime'][:,:2])) - # average across strides + # Average across all strides. doubleSupportTime = np.mean(doubleSupportTime) return doubleSupportTime def compute_gait_frame(self): - # Create frame for each gait cycle with x: pelvis heading, - # z: average vector between ASIS during gait cycle, y: cross. + + # Create frame for each gait cycle with x: pelvis heading, + # z: average vector between ASIS during gait cycle, y: cross. - # Pelvis center trajectory (for overground heading vector) + # Pelvis center trajectory (for overground heading vector). pelvisMarkerNames = ['r.ASIS_study','L.ASIS_study','r.PSIS_study','L.PSIS_study'] pelvisMarkers = [self.markerDict['markers'][mkr] for mkr in pelvisMarkerNames] pelvisCenter = np.mean(np.array(pelvisMarkers),axis=0) - # ankle trajectory (for treadmill heading vector) + # Ankle trajectory (for treadmill heading vector). leg = self.gaitEvents['ipsilateralLeg'] if leg == 'l': leg='L' anklePos = self.markerDict['markers'][leg + '_ankle_study'] - # vector from left ASIS to right ASIS (for mediolateral direction) + # Vector from left ASIS to right ASIS (for mediolateral direction). asisMarkerNames = ['L.ASIS_study','r.ASIS_study'] asisMarkers = [self.markerDict['markers'][mkr] for mkr in asisMarkerNames] asisVector = np.squeeze(np.diff(np.array(asisMarkers),axis=0)) - # heading vector per gait cycle - # if overground, use pelvis center trajectory; treadmill: ankle trajectory + # Heading vector per gait cycle. + # If overground, use pelvis center trajectory; treadmill: ankle trajectory. if self.treadmillSpeed == 0: x = np.diff(pelvisCenter[self.gaitEvents['ipsilateralIdx'][:,(0,2)],:],axis=1)[:,0,:] x = x / np.linalg.norm(x,axis=1,keepdims=True) @@ -238,22 +249,23 @@ def compute_gait_frame(self): anklePos[self.gaitEvents['ipsilateralIdx'][i,1]] x = x / np.linalg.norm(x,axis=1,keepdims=True) - # mean ASIS vector over gait cycle + # Mean ASIS vector over gait cycle. z = np.zeros((self.nGaitCycles,3)) for i in range(self.nGaitCycles): z[i,:] = np.mean(asisVector[self.gaitEvents['ipsilateralIdx'][i,0]: \ self.gaitEvents['ipsilateralIdx'][i,2]],axis=0) z = z / np.linalg.norm(z,axis=1,keepdims=True) - # cross to get y + # Cross to get y. y = np.cross(z,x) - # 3x3xnSteps + # 3x3xnSteps. R_lab_to_gait = np.stack((x.T,y.T,z.T),axis=1).transpose((2, 0, 1)) return R_lab_to_gait def get_leg(self): + if self.gaitEvents['ipsilateralLeg'] == 'r': leg = 'r' contLeg = 'L' @@ -275,44 +287,49 @@ def get_coordinates_normalized_time(self): for i in range(coordValues.shape[1])],axis=1)) coordinateValuesTimeNormalized = {} - # average + # Average. coordVals_mean = np.mean(np.array(coordValuesNorm),axis=0) coordinateValuesTimeNormalized['mean'] = pd.DataFrame(data=coordVals_mean, columns=colNames) - # standard deviation + # Standard deviation. if self.nGaitCycles >2: coordVals_sd = np.std(np.array(coordValuesNorm), axis=0) coordinateValuesTimeNormalized['sd'] = pd.DataFrame(data=coordVals_sd, columns=colNames) else: coordinateValuesTimeNormalized['sd'] = None - #return to dataframe + # Return to dataframe. coordinateValuesTimeNormalized['indiv'] = [pd.DataFrame(data=d, columns=colNames) for d in coordValuesNorm] return coordinateValuesTimeNormalized def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): - # n_gait_cycles = -1 finds all accessible gait cycles. Otherwise, it finds - # that many gait cycles, working backwards from end of trial. - - # subtract sacrum from foot - # visually, it looks like the position-based approach will be more robust - r_calc_rel_x = (self.markerDict['markers']['r_calc_study'] - self.markerDict[ - 'markers']['r.PSIS_study'])[:,0] - r_toe_rel_x = (self.markerDict['markers']['r_toe_study'] - self.markerDict[ - 'markers']['r.PSIS_study'])[:,0] - # repeat for left - l_calc_rel_x = (self.markerDict['markers']['L_calc_study'] - self.markerDict[ - 'markers']['L.PSIS_study'])[:,0] - l_toe_rel_x = (self.markerDict['markers']['L_toe_study'] - self.markerDict[ - 'markers']['L.PSIS_study'])[:,0] - - # Find HS + # n_gait_cycles = -1 finds all accessible gait cycles. Otherwise, it + # finds that many gait cycles, working backwards from end of trial. + + # Subtract sacrum from foot. + # It looks like the position-based approach will be more robust. + r_calc_rel_x = ( + self.markerDict['markers']['r_calc_study'] - + self.markerDict['markers']['r.PSIS_study'])[:,0] + r_toe_rel_x = ( + self.markerDict['markers']['r_toe_study'] - + self.markerDict['markers']['r.PSIS_study'])[:,0] + + # Repeat for left. + l_calc_rel_x = ( + self.markerDict['markers']['L_calc_study'] - + self.markerDict['markers']['L.PSIS_study'])[:,0] + l_toe_rel_x = ( + self.markerDict['markers']['L_toe_study'] - + self.markerDict['markers']['L.PSIS_study'])[:,0] + + # Find HS. rHS, _ = find_peaks(r_calc_rel_x, prominence=0.3) lHS, _ = find_peaks(l_calc_rel_x, prominence=0.3) - # Find TO + # Find TO. rTO, _ = find_peaks(-r_toe_rel_x, prominence=0.3) lTO, _ = find_peaks(-l_toe_rel_x, prominence=0.3) @@ -333,15 +350,15 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): plt.scatter(self.markerDict['time'][lTO], l_toe_rel_x[lTO], color='blue', label='lTO') plt.legend() - # find the number of gait cycles for the foot of interest + # Find the number of gait cycles for the foot of interest. if leg=='auto': - # find the last HS of either foot + # Find the last HS of either foot. if rHS[-1] > lHS[-1]: leg = 'r' else: leg = 'l' - # find the number of gait cycles for the foot of interest + # Find the number of gait cycles for the foot of interest. if leg == 'r': hsIps = rHS toIps = rTO @@ -354,34 +371,36 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): toCont = rTO if len(hsIps)-1 < n_gait_cycles: - print('You requested {} gait cycles, but only {} were found. Proceeding with this number.'.format( - n_gait_cycles,len(hsIps)-1)) + print('You requested {} gait cycles, but only {} were found. ' + 'Proceeding with this number.'.format(n_gait_cycles,len(hsIps)-1)) n_gait_cycles = len(hsIps)-1 if n_gait_cycles == -1: n_gait_cycles = len(hsIps)-1 print('Processing {} gait cycles.'.format(n_gait_cycles)) - # Ipsilateral gait events: heel strike, toe-off, heel strike + # Ipsilateral gait events: heel strike, toe-off, heel strike. gaitEvents_ips = np.zeros((n_gait_cycles, 3),dtype=np.int) - # Contralateral gait events: toe-off, heel strike + # Contralateral gait events: toe-off, heel strike. gaitEvents_cont = np.zeros((n_gait_cycles, 2),dtype=np.int) if n_gait_cycles <1: raise Exception('Not enough gait cycles found.') for i in range(n_gait_cycles): - # ipsilateral HS, TO, HS + # Ipsilateral HS, TO, HS. gaitEvents_ips[i,0] = hsIps[-i-2] gaitEvents_ips[i,2] = hsIps[-i-1] - # iterate in reverse through ipsilateral TO, finding the one that is within the range of gaitEvents_ips + # Iterate in reverse through ipsilateral TO, finding the one that + # is within the range of gaitEvents_ips. toIpsFound = False for j in range(len(toIps)): if toIps[-j-1] > gaitEvents_ips[i,0] and toIps[-j-1] < gaitEvents_ips[i,2] and not toIpsFound: gaitEvents_ips[i,1] = toIps[-j-1] toIpsFound = True - # contralateral TO, HS - # iterate in reverse through contralateral HS and TO, finding the one that is within the range of gaitEvents_ips + # Contralateral TO, HS. + # Iterate in reverse through contralateral HS and TO, finding the + # one that is within the range of gaitEvents_ips hsContFound = False toContFound = False for j in range(len(toCont)): @@ -394,13 +413,13 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): gaitEvents_cont[i,1] = hsCont[-j-1] hsContFound = True - # making contralateral gait events optional + # Making contralateral gait events optional. if not toContFound or not hsContFound: raise Warning('Could not find contralateral gait event within ipsilateral gait event range.') gaitEvents_cont[i,0] = np.nan gaitEvents_cont[i,1] = np.nan - # convert gaitEvents to times using self.markerDict['time'] + # Convert gaitEvents to times using self.markerDict['time']. gaitEventTimes_ips = self.markerDict['time'][gaitEvents_ips] gaitEventTimes_cont = self.markerDict['time'][gaitEvents_cont] @@ -412,7 +431,5 @@ def segment_walking(self, n_gait_cycles=-1, leg='auto', visualize=False): 'eventNamesContralateral':['TO','HS'], 'ipsilateralLeg':leg} - return gaitEvents - - # Lazy loading some things that are computationally expensive + return gaitEvents \ No newline at end of file From a7384697f340a9e5a7a349ba2bf978cb91389ed0 Mon Sep 17 00:00:00 2001 From: Antoine Falisse Date: Thu, 31 Aug 2023 17:11:39 -0700 Subject: [PATCH 21/29] further cleaning --- ActivityAnalyses/gait_analysis.py | 2 +- Examples/example_gait_analysis.py | 81 ++++++++++++++------------- Examples/example_walking_opensimAD.py | 32 +++++------ utils.py | 12 ++-- utilsKinematics.py | 18 +++--- utilsProcessing.py | 5 +- 6 files changed, 78 insertions(+), 72 deletions(-) diff --git a/ActivityAnalyses/gait_analysis.py b/ActivityAnalyses/gait_analysis.py index 53fa17fd..825a4aca 100644 --- a/ActivityAnalyses/gait_analysis.py +++ b/ActivityAnalyses/gait_analysis.py @@ -20,7 +20,7 @@ import os import sys -sys.path.append('../../') +sys.path.append('../') import numpy as np import pandas as pd diff --git a/Examples/example_gait_analysis.py b/Examples/example_gait_analysis.py index 8c26a22d..82d5c544 100644 --- a/Examples/example_gait_analysis.py +++ b/Examples/example_gait_analysis.py @@ -29,58 +29,62 @@ sys.path.append("../ActivityAnalyses") from gait_analysis import gait_analysis -import utils +from utils import get_trial_id, download_trial, plot_subplots_with_shading +# %% Paths. +baseDir = os.path.join(os.getcwd(), '..') +dataFolder = os.path.join(baseDir, 'Data') -# %% User-defined variables +# %% User-defined variables. +# Select example: options are treadmill and overground. +example = 'treadmill' -# overground trial -# session_id = 'b39b10d1-17c7-4976-b06c-a6aaf33fead2' -# trial_name = 'gait_3' +if example == 'treadmill': + session_id = '4d5c3eb1-1a59-4ea1-9178-d3634610561c' # 1.25m/s + trial_name = 'walk_1_25ms' -# treadmill trial 1.25m/s -session_id = '4d5c3eb1-1a59-4ea1-9178-d3634610561c' -trial_name = 'walk_1_25ms' +elif example == 'overground': + session_id = 'b39b10d1-17c7-4976-b06c-a6aaf33fead2' + trial_name = 'gait_3' scalar_names = {'gait_speed','stride_length','step_width','cadence', 'single_support_time','double_support_time'} -# how many gait cycles you'd like to analyze -# -1 for all gait cycles detected in the trial +# Select how many gait cycles you'd like to analyze. Select -1 for all gait +# cycles detected in the trial. n_gait_cycles = -1 -# Lowpass filter frequency for kinematics data +# Select lowpass filter frequency for kinematics data. filter_frequency = 6 -# %% Gait analysis - -# Get trial id from name -trial_id = utils.get_trial_id(session_id,trial_name) - -# Local data dir -> will be deleted with lambda instance -sessionDir = os.path.join(os.path.abspath('../Data'),session_id) - -# download data -trialName = utils.download_trial(trial_id,sessionDir,session_id=session_id) - -# init gait analysis -gait_r = gait_analysis(sessionDir, trialName, leg='r', - lowpass_cutoff_frequency_for_coordinate_values=filter_frequency, - n_gait_cycles=n_gait_cycles) -gait_l = gait_analysis(sessionDir, trialName, leg='l', - lowpass_cutoff_frequency_for_coordinate_values=filter_frequency, - n_gait_cycles=n_gait_cycles) +# %% Gait analysis. +# Get trial id from name. +trial_id = get_trial_id(session_id,trial_name) + +# Set session path. +sessionDir = os.path.join(dataFolder, session_id) + +# Download data. +trialName = download_trial(trial_id,sessionDir,session_id=session_id) + +# Init gait analysis. +gait_r = gait_analysis( + sessionDir, trialName, leg='r', + lowpass_cutoff_frequency_for_coordinate_values=filter_frequency, + n_gait_cycles=n_gait_cycles) +gait_l = gait_analysis( + sessionDir, trialName, leg='l', + lowpass_cutoff_frequency_for_coordinate_values=filter_frequency, + n_gait_cycles=n_gait_cycles) -# compute scalars and get time-normalized kinematic curves +# Compute scalars and get time-normalized kinematic curves. gaitResults = {} gaitResults['scalars_r'] = gait_r.compute_scalars(scalar_names) gaitResults['curves_r'] = gait_r.get_coordinates_normalized_time() gaitResults['scalars_l'] = gait_l.compute_scalars(scalar_names) -gaitResults['curves_l'] = gait_l.get_coordinates_normalized_time() - - -# %% Print scalar results +gaitResults['curves_l'] = gait_l.get_coordinates_normalized_time() +# %% Print scalar results. print('\nRight foot gait metrics:') print('(units: m and s)') print('-----------------') @@ -95,9 +99,8 @@ rounded_value = round(value, 2) print(f"{key}: {rounded_value}") -# %% Plot kinematic curves - +# %% Plot kinematic curves. for leg in ['r','l']: - utils.plot_subplots_with_shading(gaitResults['curves_' + leg]['mean'], - gaitResults['curves_' + leg]['sd'], columns=None, - leg=leg) \ No newline at end of file + plot_subplots_with_shading( + gaitResults['curves_' + leg]['mean'], + gaitResults['curves_' + leg]['sd'], columns=None, leg=leg) diff --git a/Examples/example_walking_opensimAD.py b/Examples/example_walking_opensimAD.py index 6f19cc59..28f3b649 100644 --- a/Examples/example_walking_opensimAD.py +++ b/Examples/example_walking_opensimAD.py @@ -90,23 +90,21 @@ # Insert the time interval you want to simulate. It is recommended to simulate # trials shorter than 2s (more details above). Set to [] to simulate full trial. # We here selected a time window that corresponds to a full gait stride in order -# to use poriodic constraints - -# automatically -# time_window, gaitObject = segment_gait(session_id, trial_name, dataFolder, gait_cycles_from_end=3) - -# manually -time_window = [5.7333333, 6.9333333] - -# Insert the speed of the treadmill in m/s. A positive value indicates that the -# subject is moving forward. You should ignore this parameter or set it to 0 if -# the trial was not measured on a treadmill. - -# automatically -# treadmill_speed = gaitObject.treadmillSpeed - -#manually -treadmill_speed = 1.25 +# to use periodic constraints. You can use the gait segmentation function to +# automatically segment the gait cycle. Also insert the speed of the treadmill +# in m/s. A positive value indicates that the subject is moving forward. +# You should ignore this parameter or set it to 0 if the trial was not measured +# on a treadmill. You can also use the gait segmenter to automatically extract +# the treadmill speed. +segmentation_method = 'manual' +if segmentation_method == 'automatic': + time_window, gaitObject = segment_gait( + session_id, trial_name, dataFolder, gait_cycles_from_end=3) + treadmill_speed = gaitObject.treadmillSpeed + +else: + time_window = [5.7333333, 6.9333333] + treadmill_speed = 1.25 # %% Sub-example 1: walking simulation with torque-driven model. # Insert a string to "name" you case. diff --git a/utils.py b/utils.py index 175d4cc0..bc5fd4e1 100644 --- a/utils.py +++ b/utils.py @@ -137,16 +137,16 @@ def get_model_and_metadata(session_id, session_path): return modelName -def get_model_from_metadata(sessionFolder,appendText='_scaled'): +def get_model_name_from_metadata(sessionFolder,appendText='_scaled'): metadataPath = os.path.join(sessionFolder,'sessionMetadata.yaml') if os.path.exists(metadataPath): metadata = import_metadata(os.path.join(sessionFolder,'sessionMetadata.yaml')) - modelFile = metadata['openSimModel'] + appendText + '.osim' + modelName = metadata['openSimModel'] + appendText + '.osim' else: raise Exception('Session metadata not found, could not identify OpenSim model.') - return modelFile + return modelName def get_motion_data(trial_id, session_path): @@ -257,7 +257,7 @@ def download_kinematics(session_id, folder=None, trialNames=None): return loadedTrialNames, modelName -# %% download pertinent trial data +# Download pertinent trial data. def download_trial(trial_id, folder, session_id=None): trial = get_trial_json(trial_id) @@ -273,7 +273,9 @@ def download_trial(trial_id, folder, session_id=None): get_motion_data(trial_id,folder) return trial['name'] -# %% get trialID from name + + +# Get trial ID from name. def get_trial_id(session_id,trial_name): session = get_session_json(session_id) diff --git a/utilsKinematics.py b/utilsKinematics.py index d591d1c8..4fa06e55 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -43,8 +43,9 @@ def __init__(self, sessionDir, trialName, modelPath = os.path.join(sessionDir, 'OpenSimData', 'Model', '{}.osim'.format(modelName)) if not os.path.exists(modelPath): - modelFromMetadata = utils.get_model_from_metadata(sessionDir) - modelPath = modelPath.replace(modelName + '.osim',modelFromMetadata) + modelNameFromMetadata = utils.get_model_name_from_metadata(sessionDir) + modelPath = modelPath.replace(modelName + '.osim', + modelNameFromMetadata) self.model = opensim.Model(modelPath) self.model.initSystem() @@ -60,8 +61,8 @@ def __init__(self, sessionDir, trialName, tableProcessor.append(opensim.TabOpUseAbsoluteStateNames()) self.time = np.asarray(self.table.getIndependentColumn()) - # initialize the state trajectory. We will set it in other functions - # if it is needed + # Initialize the state trajectory. We will set it in other functions + # if it is needed. self._stateTrajectory = None # Filter coordinate values. @@ -126,7 +127,7 @@ def __init__(self, sessionDir, trialName, self.coordinates = [self.coordinateSet.get(i).getName() for i in range(self.nCoordinates)] - # Find rotational and translational coords + # Find rotational and translational coordinates. self.idxColumnTrLabels = [ self.columnLabels.index(i) for i in self.coordinates if \ self.coordinateSet.get(i).getMotionType() == 2] @@ -147,11 +148,12 @@ def __init__(self, sessionDir, trialName, 'arm_flex_l', 'arm_add_l', 'arm_rot_l', 'elbow_flex_l', 'pro_sup_l'] - # Only set the state trajectory when needed b/c it is slow + # Only set the state trajectory when needed because it is slow. def stateTrajectory(self): if self._stateTrajectory is None: - self._stateTrajectory = opensim.StatesTrajectory.createFromStatesTable( - self.model, self.table) + self._stateTrajectory = ( + opensim.StatesTrajectory.createFromStatesTable( + self.model, self.table)) return self._stateTrajectory def get_coordinate_values(self, in_degrees=True, diff --git a/utilsProcessing.py b/utilsProcessing.py index b4d82946..bbddc44e 100644 --- a/utilsProcessing.py +++ b/utilsProcessing.py @@ -18,10 +18,11 @@ limitations under the License. ''' +import os +pathFile = os.path.dirname(os.path.realpath(__file__)) import sys -sys.path.append('AnalysisFunctions/') +sys.path.append(os.path.join(pathFile, 'ActivityAnalyses')) -import os import logging import opensim import numpy as np From 7ad8f3bf9ae055d29e6bca56123d53ea8de79459 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 1 Sep 2023 11:47:01 -0700 Subject: [PATCH 22/29] explicit model loading --- utilsKinematics.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/utilsKinematics.py b/utilsKinematics.py index 4fa06e55..dc915fbe 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -31,7 +31,7 @@ class kinematics: def __init__(self, sessionDir, trialName, - modelName='LaiUhlrich2022_scaled', + modelName=None, lowpass_cutoff_frequency_for_coordinate_values=-1): self.lowpass_cutoff_frequency_for_coordinate_values = ( @@ -39,13 +39,19 @@ def __init__(self, sessionDir, trialName, # Model. opensim.Logger.setLevelString('error') - # check if specified model name exists - modelPath = os.path.join(sessionDir, 'OpenSimData', 'Model', + + modelBasePath = os.path.join(sessionDir, 'OpenSimData', 'Model') + # Load model if specified, otherwise load the one that was on server + if modelName is None: + modelNameFromMetadata = utils.get_model_name_from_metadata(sessionDir) + modelPath = os.path.join(modelBasePath,modelNameFromMetadata) + else: + modelPath = os.path.join(modelBasePath, '{}.osim'.format(modelName)) + + # make sure model exists if not os.path.exists(modelPath): - modelNameFromMetadata = utils.get_model_name_from_metadata(sessionDir) - modelPath = modelPath.replace(modelName + '.osim', - modelNameFromMetadata) + raise Exception('Model path: ' + modelPath + ' does not exist.') self.model = opensim.Model(modelPath) self.model.initSystem() From 3112a6c892d96353726431066bbc024c9baaa31d Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 1 Sep 2023 12:01:52 -0700 Subject: [PATCH 23/29] make gait segmentation consistent --- Examples/example_walking_opensimAD.py | 8 +++++++- utilsProcessing.py | 13 +++++++++---- 2 files changed, 16 insertions(+), 5 deletions(-) diff --git a/Examples/example_walking_opensimAD.py b/Examples/example_walking_opensimAD.py index 28f3b649..92b8bc7a 100644 --- a/Examples/example_walking_opensimAD.py +++ b/Examples/example_walking_opensimAD.py @@ -61,6 +61,7 @@ from mainOpenSimAD import run_tracking from utilsAuthentication import get_token from utilsProcessing import segment_gait +from utils import get_trial_id, download_trial # %% OpenCap authentication. Visit https://app.opencap.ai/login to create an # account if you don't have one yet. @@ -96,8 +97,13 @@ # You should ignore this parameter or set it to 0 if the trial was not measured # on a treadmill. You can also use the gait segmenter to automatically extract # the treadmill speed. -segmentation_method = 'manual' +segmentation_method = 'automatic' if segmentation_method == 'automatic': + # Download the trial + download_trial(get_trial_id(session_id,trial_name), + os.path.join(dataFolder,session_id), + session_id=session_id) + time_window, gaitObject = segment_gait( session_id, trial_name, dataFolder, gait_cycles_from_end=3) treadmill_speed = gaitObject.treadmillSpeed diff --git a/utilsProcessing.py b/utilsProcessing.py index bbddc44e..91139c93 100644 --- a/utilsProcessing.py +++ b/utilsProcessing.py @@ -42,10 +42,9 @@ def lowPassFilter(time, data, lowpass_cutoff_frequency, order=4): # %% Segment gait def segment_gait(session_id, trial_name, data_folder, gait_cycles_from_end=0): - from gait_analysis import gait_analysis - - trial_id = get_trial_id(session_id,trial_name) - download_trial(trial_id,os.path.join(data_folder,session_id),session_id=session_id) + # Segmentation is done in the gait_analysis class + from gait_analysis import gait_analysis + gait = gait_analysis(os.path.join(data_folder,session_id), trial_name, n_gait_cycles=-1) heelstrikeTimes = gait.gaitEvents['ipsilateralTime'][gait_cycles_from_end,(0,2)].tolist() @@ -56,6 +55,9 @@ def segment_gait(session_id, trial_name, data_folder, gait_cycles_from_end=0): def segment_squats(ikFilePath, pelvis_ty=None, timeVec=None, visualize=False, filter_pelvis_ty=True, cutoff_frequency=4, height=.2): + # TODO: eventually, this belongs in a squat_analysis class and should take + # the form of segment_gait + # Extract pelvis_ty if not given. if pelvis_ty is None and timeVec is None: ikResults = storage_to_dataframe(ikFilePath,headers={'pelvis_ty'}) @@ -118,6 +120,9 @@ def segment_STS(ikFilePath, pelvis_ty=None, timeVec=None, velSeated=0.3, velStanding=0.15, visualize=False, filter_pelvis_ty=True, cutoff_frequency=4, delay=0.1): + # TODO: eventually, this belongs in a sts_analysis class and should take + # the form of segment_gait + # Extract pelvis_ty if not given. if pelvis_ty is None and timeVec is None: ikResults = storage_to_dataframe(ikFilePath,headers={'pelvis_ty'}) From dafcbc43a704ab1c556085760d6b66ecfdcdcb63 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 1 Sep 2023 17:29:56 -0700 Subject: [PATCH 24/29] clean up plotting --- Examples/example_gait_analysis.py | 18 ++++-- utils.py | 59 +------------------ utilsPlotting.py | 95 ++++++++++++++++++++++++++++++- 3 files changed, 107 insertions(+), 65 deletions(-) diff --git a/Examples/example_gait_analysis.py b/Examples/example_gait_analysis.py index 82d5c544..2a368dfb 100644 --- a/Examples/example_gait_analysis.py +++ b/Examples/example_gait_analysis.py @@ -29,7 +29,8 @@ sys.path.append("../ActivityAnalyses") from gait_analysis import gait_analysis -from utils import get_trial_id, download_trial, plot_subplots_with_shading +from utils import get_trial_id, download_trial +from utilsPlotting import plot_dataframe_with_shading # %% Paths. baseDir = os.path.join(os.getcwd(), '..') @@ -99,8 +100,13 @@ rounded_value = round(value, 2) print(f"{key}: {rounded_value}") -# %% Plot kinematic curves. -for leg in ['r','l']: - plot_subplots_with_shading( - gaitResults['curves_' + leg]['mean'], - gaitResults['curves_' + leg]['sd'], columns=None, leg=leg) + +# %% You can plot multiple curves, in this case we compare right and left legs + +plot_dataframe_with_shading( + [gaitResults['curves_r']['mean'], gaitResults['curves_l']['mean']], + [gaitResults['curves_r']['sd'], gaitResults['curves_l']['sd']], + leg = ['r','l'], + xlabel = '% gait cycle', + title = 'kinematics (m or deg)', + legend_entries = ['right','left']) \ No newline at end of file diff --git a/utils.py b/utils.py index bc5fd4e1..e5747536 100644 --- a/utils.py +++ b/utils.py @@ -633,61 +633,4 @@ def zipdir(path, ziph): if writeToDB: post_file_to_trial(session_zip,dynamic_ids[-1],tag='session_zip', device_id='all') - - - -def plot_subplots_with_shading(mean_df, sd_df, columns=None, leg=None): - if columns is None: - columns = [col for col in mean_df.columns if col != 'time'] - - columns = [col for col in columns if not any(sub in col for sub in ['_beta', 'mtp', 'time'])] - - if leg == 'r': - columns = [col for col in columns if not col.endswith('_l')] - elif leg == 'l': - columns = [col for col in columns if not col.endswith('_r')] - - num_columns = len(columns) - num_rows = (num_columns + 3) // 4 # Always 4 columns per row - - fig, axes = plt.subplots(num_rows, 4, figsize=(12, 8)) - axes = axes.flatten() - - x_values = np.array([0, 50, 100]) - x_labels = ['0%', '50%', '100%'] - - for i, column in enumerate(columns): - row = i // 4 - ax = axes[i] - - mean_values = mean_df[column] - - ax.plot(mean_values, color='black', label='Mean') - - # Check if sd_df is not None before plotting - if sd_df is not None: - sd_values = sd_df[column] - ax.fill_between( - range(len(mean_values)), - mean_values - sd_values, - mean_values + sd_values, - color='gray', - alpha=0.5, - label='Mean ± SD' - ) - - ax.set_xticks(x_values) - ax.set_xticklabels(x_labels) - ax.set_xlabel('% gait cycle' if row == num_rows - 1 else None, fontsize=16) - ax.set_ylabel(column, fontsize=16) - - # Increase font size for axis labels - ax.tick_params(axis='both', which='major', labelsize=12) - - # Remove any unused subplots - if num_columns < num_rows * 4: - for i in range(num_columns, num_rows * 4): - fig.delaxes(axes[i]) - - plt.tight_layout() - plt.show() \ No newline at end of file + \ No newline at end of file diff --git a/utilsPlotting.py b/utilsPlotting.py index 92f01d2e..fe9ea28e 100644 --- a/utilsPlotting.py +++ b/utilsPlotting.py @@ -113,4 +113,97 @@ def plot_dataframe(dataframes, x=None, y=[], xlabel=None, ylabel=None, # Show plot (needed if running through terminal). plt.show() - \ No newline at end of file + + +def plot_dataframe_with_shading(mean_dataframe, sd_dataframe=None, y=None, + leg=None, xlabel=None, title=None, legend_entries=None): + if not isinstance(mean_dataframe, list): + mean_dataframe = [mean_dataframe] + + if sd_dataframe is not None: + if not isinstance(sd_dataframe, list): + sd_dataframe = [sd_dataframe] + + if not isinstance(leg, list): + leg = [leg] * len(mean_dataframe) + + if y is None: + y = [col for col in mean_dataframe[0].columns if col != 'time'] + + columns = [col for col in y if not any(sub in col for sub in ['_beta', 'mtp', 'time'])] + + if leg[0] == 'r': + columns = [col for col in columns if not col.endswith('_l')] + elif leg[0] == 'l': + columns = [col for col in columns if not col.endswith('_r')] + + num_columns = len(columns) + num_rows = (num_columns + 3) // 4 # Always 4 columns per row + + colormap = plt.cm.get_cmap('viridis', len(mean_dataframe)) + + fig, axes = plt.subplots(num_rows, 4, figsize=(12, 8)) + axes = axes.flatten() + + for i, column in enumerate(columns): + row = i // 4 + ax = axes[i] + + for j, (mean_df, sd_df) in enumerate(zip(mean_dataframe, sd_dataframe)): + if len(mean_dataframe) > 1: + color = colormap(j) + else: + color = 'black' + + if leg[j] is not None and (column.endswith('_r') or column.endswith('_l')): + col=column[:-2] + '_' + leg[j] + colLabel = column[:-2] + else: + col = column + colLabel = column + + mean_values = mean_df[col] + + if legend_entries is None: + thisLegend = [] + else: + thisLegend = legend_entries[j] + + ax.plot(mean_values, color=color, label=thisLegend) + + # Check if sd_df is not None before plotting + if sd_df is not None: + sd_column = col + if sd_column in sd_df.columns: + sd_values = sd_df[sd_column] + ax.fill_between( + range(len(mean_values)), + mean_values - sd_values, + mean_values + sd_values, + color=color, + alpha=0.3, + linewidth=0, # Remove surrounding line + ) + + + ax.set_xlabel(xlabel if row == num_rows - 1 else None, fontsize=12) + ax.set_ylabel(colLabel, fontsize=12) + + # Increase font size for axis labels + ax.tick_params(axis='both', which='major', labelsize=10) + + # Create the legend in the first subplot if legend_entries is provided + if legend_entries: + axes[0].legend() + + # Remove any unused subplots + if num_columns < num_rows * 4: + for i in range(num_columns, num_rows * 4): + fig.delaxes(axes[i]) + + # Title + if title is not None: + fig.suptitle(title) + + plt.tight_layout() + plt.show() From a6ee02a377ae573e754e7322684b0ac851a94167 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 1 Sep 2023 22:36:17 -0700 Subject: [PATCH 25/29] clean --- Examples/example_gait_analysis.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Examples/example_gait_analysis.py b/Examples/example_gait_analysis.py index 2a368dfb..84b0853a 100644 --- a/Examples/example_gait_analysis.py +++ b/Examples/example_gait_analysis.py @@ -101,7 +101,7 @@ print(f"{key}: {rounded_value}") -# %% You can plot multiple curves, in this case we compare right and left legs +# %% You can plot multiple curves, in this case we compare right and left legs. plot_dataframe_with_shading( [gaitResults['curves_r']['mean'], gaitResults['curves_l']['mean']], From 6c015800e869fde6488bbfa715eca6f4c90a9495 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Fri, 1 Sep 2023 22:47:14 -0700 Subject: [PATCH 26/29] clean --- utils.py | 1 - 1 file changed, 1 deletion(-) diff --git a/utils.py b/utils.py index e5747536..7f62b7bf 100644 --- a/utils.py +++ b/utils.py @@ -29,7 +29,6 @@ import glob import zipfile import platform -import matplotlib.pyplot as plt from utilsAPI import get_api_url from utilsAuthentication import get_token From 1db8636be9aa4f0f0df813b18e39e6808a1c9901 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Sun, 3 Sep 2023 00:17:30 -0700 Subject: [PATCH 27/29] move trc to kinematics class --- ActivityAnalyses/y_balance_analysis.py | 427 +++++++++++++++++++++++++ utilsKinematics.py | 16 + 2 files changed, 443 insertions(+) create mode 100644 ActivityAnalyses/y_balance_analysis.py diff --git a/ActivityAnalyses/y_balance_analysis.py b/ActivityAnalyses/y_balance_analysis.py new file mode 100644 index 00000000..d28d4c86 --- /dev/null +++ b/ActivityAnalyses/y_balance_analysis.py @@ -0,0 +1,427 @@ +""" + --------------------------------------------------------------------------- + OpenCap processing: gaitAnalysis.py + --------------------------------------------------------------------------- + + Copyright 2023 Stanford University and the Authors + + Author(s): Antoine Falisse, Scott Uhlrich + + Licensed under the Apache License, Version 2.0 (the "License"); you may not + use this file except in compliance with the License. You may obtain a copy + of the License at http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. +""" + +import os +import sys +sys.path.append('../') + +import numpy as np +import pandas as pd +from scipy.signal import find_peaks + +from utilsKinematics import kinematics + + +class gait_analysis(kinematics): + + def __init__(self, session_dir, trial_name, leg='auto', + lowpass_cutoff_frequency_for_coordinate_values=-1, + n_gait_cycles=-1): + + # Inherit init from kinematics class. + super().__init__( + session_dir, + trial_name, + lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) + + # Marker data load and filter. + self.marker_dict = self.get_marker_dict(self, session_dir, trial_name, + lowpass_cutoff_frequency = -1) + + # Coordinate values. + self.coordinateValues = self.get_coordinate_values() + + # Segment gait cycles. + self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles,leg=leg) + self.nGaitCycles = np.shape(self.gaitEvents['ipsilateralIdx'])[0] + + # Determine treadmill speed (0 if overground). + self.treadmillSpeed = self.compute_treadmill_speed() + + # Initialize variables to be lazy loaded. + self._comValues = None + self._R_world_to_gait = None + + # Compute COM trajectory. + def comValues(self): + if self._comValues is None: + self._comValues = self.get_center_of_mass_values() + return self._comValues + + # Compute gait frame. + def R_world_to_gait(self): + if self._R_world_to_gait is None: + self._R_world_to_gait = self.compute_gait_frame() + return self._R_world_to_gait + + def compute_scalars(self,scalarNames): + + # Verify that scalarNames are methods in gait_analysis. + method_names = [func for func in dir(self) if callable(getattr(self, func))] + possibleMethods = [entry for entry in method_names if 'compute_' in entry] + + if scalarNames is None: + print('No scalars defined, these methods are available:') + print(*possibleMethods) + return + + nonexistant_methods = [entry for entry in scalarNames if 'compute_' + entry not in method_names] + + if len(nonexistant_methods) > 0: + raise Exception(str(['compute_' + a for a in nonexistant_methods]) + ' does not exist in gait_analysis class.') + + scalarDict = {} + for scalarName in scalarNames: + thisFunction = getattr(self, 'compute_' + scalarName) + scalarDict[scalarName] = thisFunction() + + return scalarDict + + def compute_stride_length(self): + + leg,_ = self.get_leg() + + calc_position = self.markerDict['markers'][leg + '_calc_study'] + + # On treadmill, the stride length is the difference in ipsilateral + # calcaneus position at heel strike + treadmill speed * time. + strideLength = ( + np.linalg.norm( + calc_position[self.gaitEvents['ipsilateralIdx'][:,:1]] - + calc_position[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) + + self.treadmillSpeed * np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)])) + + # Average across all strides. + strideLength = np.mean(strideLength) + + return strideLength + + def compute_gait_speed(self): + + comValuesArray = np.vstack((self.comValues()['x'],self.comValues()['y'],self.comValues()['z'])).T + gait_speed = ( + np.linalg.norm( + comValuesArray[self.gaitEvents['ipsilateralIdx'][:,:1]] - + comValuesArray[self.gaitEvents['ipsilateralIdx'][:,2:3]], axis=2) / + np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + self.treadmillSpeed) + + # Average across all strides. + gait_speed = np.mean(gait_speed) + + return gait_speed + + def compute_cadence(self): + + # In steps per second. + cadence = 1/np.diff(self.gaitEvents['ipsilateralTime'][:,(0,2)]) + + # Average across all strides. + cadence = np.mean(cadence) + + return cadence + + def compute_treadmill_speed(self, overground_speed_threshold=0.3): + + leg,_ = self.get_leg() + + foot_position = self.markerDict['markers'][leg + '_ankle_study'] + + stanceTimeLength = np.round(np.diff(self.gaitEvents['ipsilateralIdx'][:,:2])) + startIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,:1]+.1*stanceTimeLength).astype(int) + endIdx = np.round(self.gaitEvents['ipsilateralIdx'][:,1:2]-.3*stanceTimeLength).astype(int) + + # Average instantaneous velocities. + dt = np.diff(self.markerDict['time'][:2])[0] + for i in range(self.nGaitCycles): + footVel = np.linalg.norm(np.mean(np.diff( + foot_position[startIdx[i,0]:endIdx[i,0],:],axis=0),axis=0)/dt) + + treadmillSpeed = np.mean(footVel) + + # Overground. + if treadmillSpeed < overground_speed_threshold: + treadmillSpeed = 0 + + return treadmillSpeed + + def compute_step_width(self): + + leg,contLeg = self.get_leg() + + # Get ankle joint center positions. + ankle_position_ips = ( + self.markerDict['markers'][leg + '_ankle_study'] + + self.markerDict['markers'][leg + '_mankle_study'])/2 + ankle_position_cont = ( + self.markerDict['markers'][contLeg + '_ankle_study'] + + self.markerDict['markers'][contLeg + '_mankle_study'])/2 + ankleVector = ( + ankle_position_cont[self.gaitEvents['contralateralIdx'][:,1]] - + ankle_position_ips[self.gaitEvents['ipsilateralIdx'][:,0]]) + + ankleVector_inGaitFrame = np.array( + [np.dot(ankleVector[i,:], self.R_world_to_gait()[i,:,:]) + for i in range(self.nGaitCycles)]) + + # Step width is z distance. + stepWidth = np.abs(ankleVector_inGaitFrame[:,2]) + + # Average across all strides. + stepWidth = np.mean(stepWidth) + + return stepWidth + + def compute_single_support_time(self): + + singleSupportTime = np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) + + # Average across all strides. + singleSupportTime = np.mean(singleSupportTime) + + return singleSupportTime + + def compute_double_support_time(self): + + # Ipsilateral single support time - contralateral swing time. + doubleSupportTime = ( + np.diff(self.gaitEvents['ipsilateralTime'][:,:2]) - + np.diff(self.gaitEvents['contralateralTime'][:,:2])) + + # Average across all strides. + doubleSupportTime = np.mean(doubleSupportTime) + + return doubleSupportTime + + def compute_gait_frame(self): + + # Create frame for each gait cycle with x: pelvis heading, + # z: average vector between ASIS during gait cycle, y: cross. + + # Pelvis center trajectory (for overground heading vector). + pelvisMarkerNames = ['r.ASIS_study','L.ASIS_study','r.PSIS_study','L.PSIS_study'] + pelvisMarkers = [self.markerDict['markers'][mkr] for mkr in pelvisMarkerNames] + pelvisCenter = np.mean(np.array(pelvisMarkers),axis=0) + + # Ankle trajectory (for treadmill heading vector). + leg = self.gaitEvents['ipsilateralLeg'] + if leg == 'l': leg='L' + anklePos = self.markerDict['markers'][leg + '_ankle_study'] + + # Vector from left ASIS to right ASIS (for mediolateral direction). + asisMarkerNames = ['L.ASIS_study','r.ASIS_study'] + asisMarkers = [self.markerDict['markers'][mkr] for mkr in asisMarkerNames] + asisVector = np.squeeze(np.diff(np.array(asisMarkers),axis=0)) + + # Heading vector per gait cycle. + # If overground, use pelvis center trajectory; treadmill: ankle trajectory. + if self.treadmillSpeed == 0: + x = np.diff(pelvisCenter[self.gaitEvents['ipsilateralIdx'][:,(0,2)],:],axis=1)[:,0,:] + x = x / np.linalg.norm(x,axis=1,keepdims=True) + else: + x = np.zeros((self.nGaitCycles,3)) + for i in range(self.nGaitCycles): + x[i,:] = anklePos[self.gaitEvents['ipsilateralIdx'][i,2]] - \ + anklePos[self.gaitEvents['ipsilateralIdx'][i,1]] + x = x / np.linalg.norm(x,axis=1,keepdims=True) + + # Mean ASIS vector over gait cycle. + z = np.zeros((self.nGaitCycles,3)) + for i in range(self.nGaitCycles): + z[i,:] = np.mean(asisVector[self.gaitEvents['ipsilateralIdx'][i,0]: \ + self.gaitEvents['ipsilateralIdx'][i,2]],axis=0) + z = z / np.linalg.norm(z,axis=1,keepdims=True) + + # Cross to get y. + y = np.cross(z,x) + + # 3x3xnSteps. + R_lab_to_gait = np.stack((x.T,y.T,z.T),axis=1).transpose((2, 0, 1)) + + return R_lab_to_gait + + def get_leg(self): + + if self.gaitEvents['ipsilateralLeg'] == 'r': + leg = 'r' + contLeg = 'L' + else: + leg = 'L' + contLeg = 'r' + + return leg, contLeg + + def get_coordinates_normalized_time(self): + + colNames = self.coordinateValues.columns + data = self.coordinateValues.to_numpy(copy=True) + coordValuesNorm = [] + for i in range(self.nGaitCycles): + coordValues = data[self.gaitEvents['ipsilateralIdx'][i,0]:self.gaitEvents['ipsilateralIdx'][i,2]] + coordValuesNorm.append(np.stack([np.interp(np.linspace(0,100,101), + np.linspace(0,100,len(coordValues)),coordValues[:,i]) \ + for i in range(coordValues.shape[1])],axis=1)) + + coordinateValuesTimeNormalized = {} + # Average. + coordVals_mean = np.mean(np.array(coordValuesNorm),axis=0) + coordinateValuesTimeNormalized['mean'] = pd.DataFrame(data=coordVals_mean, columns=colNames) + + # Standard deviation. + if self.nGaitCycles >2: + coordVals_sd = np.std(np.array(coordValuesNorm), axis=0) + coordinateValuesTimeNormalized['sd'] = pd.DataFrame(data=coordVals_sd, columns=colNames) + else: + coordinateValuesTimeNormalized['sd'] = None + + # Return to dataframe. + coordinateValuesTimeNormalized['indiv'] = [pd.DataFrame(data=d, columns=colNames) for d in coordValuesNorm] + + return coordinateValuesTimeNormalized + + def segment_y_balance(self, n_gait_cycles=-1, leg='auto', visualize=False): + + # n_gait_cycles = -1 finds all accessible gait cycles. Otherwise, it + # finds that many gait cycles, working backwards from end of trial. + + # Subtract sacrum from foot. + # It looks like the position-based approach will be more robust. + r_calc_rel_x = ( + self.markerDict['markers']['r_calc_study'] - + self.markerDict['markers']['r.PSIS_study'])[:,0] + r_toe_rel_x = ( + self.markerDict['markers']['r_toe_study'] - + self.markerDict['markers']['r.PSIS_study'])[:,0] + + # Repeat for left. + l_calc_rel_x = ( + self.markerDict['markers']['L_calc_study'] - + self.markerDict['markers']['L.PSIS_study'])[:,0] + l_toe_rel_x = ( + self.markerDict['markers']['L_toe_study'] - + self.markerDict['markers']['L.PSIS_study'])[:,0] + + # Find HS. + rHS, _ = find_peaks(r_calc_rel_x, prominence=0.3) + lHS, _ = find_peaks(l_calc_rel_x, prominence=0.3) + + # Find TO. + rTO, _ = find_peaks(-r_toe_rel_x, prominence=0.3) + lTO, _ = find_peaks(-l_toe_rel_x, prominence=0.3) + + if visualize: + import matplotlib.pyplot as plt + plt.close('all') + plt.figure(1) + plt.plot(self.markerDict['time'],r_toe_rel_x,label='toe') + plt.plot(self.markerDict['time'],r_calc_rel_x,label='calc') + plt.scatter(self.markerDict['time'][rHS], r_calc_rel_x[rHS], color='red', label='rHS') + plt.scatter(self.markerDict['time'][rTO], r_toe_rel_x[rTO], color='blue', label='rTO') + plt.legend() + + plt.figure(2) + plt.plot(self.markerDict['time'],l_toe_rel_x,label='toe') + plt.plot(self.markerDict['time'],l_calc_rel_x,label='calc') + plt.scatter(self.markerDict['time'][lHS], l_calc_rel_x[lHS], color='red', label='lHS') + plt.scatter(self.markerDict['time'][lTO], l_toe_rel_x[lTO], color='blue', label='lTO') + plt.legend() + + # Find the number of gait cycles for the foot of interest. + if leg=='auto': + # Find the last HS of either foot. + if rHS[-1] > lHS[-1]: + leg = 'r' + else: + leg = 'l' + + # Find the number of gait cycles for the foot of interest. + if leg == 'r': + hsIps = rHS + toIps = rTO + hsCont = lHS + toCont = lTO + elif leg == 'l': + hsIps = lHS + toIps = lTO + hsCont = rHS + toCont = rTO + + if len(hsIps)-1 < n_gait_cycles: + print('You requested {} gait cycles, but only {} were found. ' + 'Proceeding with this number.'.format(n_gait_cycles,len(hsIps)-1)) + n_gait_cycles = len(hsIps)-1 + if n_gait_cycles == -1: + n_gait_cycles = len(hsIps)-1 + print('Processing {} gait cycles.'.format(n_gait_cycles)) + + # Ipsilateral gait events: heel strike, toe-off, heel strike. + gaitEvents_ips = np.zeros((n_gait_cycles, 3),dtype=np.int) + # Contralateral gait events: toe-off, heel strike. + gaitEvents_cont = np.zeros((n_gait_cycles, 2),dtype=np.int) + if n_gait_cycles <1: + raise Exception('Not enough gait cycles found.') + + for i in range(n_gait_cycles): + # Ipsilateral HS, TO, HS. + gaitEvents_ips[i,0] = hsIps[-i-2] + gaitEvents_ips[i,2] = hsIps[-i-1] + + # Iterate in reverse through ipsilateral TO, finding the one that + # is within the range of gaitEvents_ips. + toIpsFound = False + for j in range(len(toIps)): + if toIps[-j-1] > gaitEvents_ips[i,0] and toIps[-j-1] < gaitEvents_ips[i,2] and not toIpsFound: + gaitEvents_ips[i,1] = toIps[-j-1] + toIpsFound = True + + # Contralateral TO, HS. + # Iterate in reverse through contralateral HS and TO, finding the + # one that is within the range of gaitEvents_ips + hsContFound = False + toContFound = False + for j in range(len(toCont)): + if toCont[-j-1] > gaitEvents_ips[i,0] and toCont[-j-1] < gaitEvents_ips[i,2] and not toContFound: + gaitEvents_cont[i,0] = toCont[-j-1] + toContFound = True + + for j in range(len(hsCont)): + if hsCont[-j-1] > gaitEvents_ips[i,0] and hsCont[-j-1] < gaitEvents_ips[i,2] and not hsContFound: + gaitEvents_cont[i,1] = hsCont[-j-1] + hsContFound = True + + # Making contralateral gait events optional. + if not toContFound or not hsContFound: + raise Warning('Could not find contralateral gait event within ipsilateral gait event range.') + gaitEvents_cont[i,0] = np.nan + gaitEvents_cont[i,1] = np.nan + + # Convert gaitEvents to times using self.markerDict['time']. + gaitEventTimes_ips = self.markerDict['time'][gaitEvents_ips] + gaitEventTimes_cont = self.markerDict['time'][gaitEvents_cont] + + gaitEvents = {'ipsilateralIdx':gaitEvents_ips, + 'contralateralIdx':gaitEvents_cont, + 'ipsilateralTime':gaitEventTimes_ips, + 'contralateralTime':gaitEventTimes_cont, + 'eventNamesIpsilateral':['HS','TO','HS'], + 'eventNamesContralateral':['TO','HS'], + 'ipsilateralLeg':leg} + + return gaitEvents + \ No newline at end of file diff --git a/utilsKinematics.py b/utilsKinematics.py index dc915fbe..c3bcf996 100644 --- a/utilsKinematics.py +++ b/utilsKinematics.py @@ -27,6 +27,7 @@ from utilsProcessing import lowPassFilter +from utilsTRC import trc_2_dict class kinematics: @@ -161,6 +162,21 @@ def stateTrajectory(self): opensim.StatesTrajectory.createFromStatesTable( self.model, self.table)) return self._stateTrajectory + + def get_marker_dict(self, session_dir, trial_name, + lowpass_cutoff_frequency = -1): + + trcFilePath = os.path.join(session_dir, + 'MarkerData', + '{}.trc'.format(trial_name)) + + markerDict = trc_2_dict(trcFilePath) + if lowpass_cutoff_frequency > 0: + markerDict['markers'] = { + marker_name: lowPassFilter(self.time, data, lowpass_cutoff_frequency) + for marker_name, data in markerDict['markers'].items()} + + return markerDict def get_coordinate_values(self, in_degrees=True, lowpass_cutoff_frequency=-1): From 33a348844522267a3ef4ffb827f343d45f9fe621 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Sun, 3 Sep 2023 01:05:40 -0700 Subject: [PATCH 28/29] initial y balance progress TODO: write example, segmentation, test --- ActivityAnalyses/y_balance_analysis.py | 65 ++++++++++++++++++++++---- 1 file changed, 55 insertions(+), 10 deletions(-) diff --git a/ActivityAnalyses/y_balance_analysis.py b/ActivityAnalyses/y_balance_analysis.py index d28d4c86..318cf675 100644 --- a/ActivityAnalyses/y_balance_analysis.py +++ b/ActivityAnalyses/y_balance_analysis.py @@ -42,22 +42,67 @@ def __init__(self, session_dir, trial_name, leg='auto', lowpass_cutoff_frequency_for_coordinate_values=lowpass_cutoff_frequency_for_coordinate_values) # Marker data load and filter. - self.marker_dict = self.get_marker_dict(self, session_dir, trial_name, - lowpass_cutoff_frequency = -1) + self.marker_dict = self.get_marker_dict(session_dir, trial_name, + lowpass_cutoff_frequency=lowpass_cutoff_frequency_for_coordinate_values) # Coordinate values. self.coordinateValues = self.get_coordinate_values() - # Segment gait cycles. - self.gaitEvents = self.segment_walking(n_gait_cycles=n_gait_cycles,leg=leg) - self.nGaitCycles = np.shape(self.gaitEvents['ipsilateralIdx'])[0] + # Find what leg is moving. + self.leg, self.contLeg = self.get_y_balance_leg() + + # Define reference frame. + self.R_world_to_yBalance = self.compute_y_balance_frame() + + # Segment y balance. + self.events, self.distances, self.leg = self.segment_y_balance() + + + def get_y_balance_leg(self): + + calc_r = self.markerDict['markers']['r_calc_study'] + calc_l = self.markerDict['markers']['L_calc_study'] + + leg= {} + if np.std(calc_r) < np.std(calc_l): + leg['stance'] = 'r' + leg['swing'] = 'L' + else: + leg['stance'] = 'L' + leg['swing'] = 'r' + + return leg + + def compute_y_balance_frame(self): + # y up, x-> line between calc and 2metatarsal head projected onto xz plane, + # z -> cross(x,y) + + foot = self.markerDict['markers'][self.leg + '_toe_study'] - \ + self.markerDict['markers'][self.leg + '_calc_study'] + + x = np.mean(foot[:10,:] / np.linalg.norm(foot[:10,:],axis=1,keepdims=True)) + x[:,1] = 0 + + y = np.array([0,1,0]) + z = np.cross(x,y) - # Determine treadmill speed (0 if overground). - self.treadmillSpeed = self.compute_treadmill_speed() + R_lab_to_yBalance = np.array([x.T,y.T,z.T]) - # Initialize variables to be lazy loaded. - self._comValues = None - self._R_world_to_gait = None + # TODO TEST + + return R_lab_to_yBalance + + def segment_y_balance(self): + + todo=1 + + + + + + + + # Compute COM trajectory. def comValues(self): From 812a88b1887342ffc042f56610bdab1cd8f0f638 Mon Sep 17 00:00:00 2001 From: suhlrich Date: Sun, 3 Sep 2023 01:14:20 -0700 Subject: [PATCH 29/29] comments --- ActivityAnalyses/y_balance_analysis.py | 17 ++++++++++++----- 1 file changed, 12 insertions(+), 5 deletions(-) diff --git a/ActivityAnalyses/y_balance_analysis.py b/ActivityAnalyses/y_balance_analysis.py index 318cf675..39d0b992 100644 --- a/ActivityAnalyses/y_balance_analysis.py +++ b/ActivityAnalyses/y_balance_analysis.py @@ -29,7 +29,7 @@ from utilsKinematics import kinematics -class gait_analysis(kinematics): +class y_balance(kinematics): def __init__(self, session_dir, trial_name, leg='auto', lowpass_cutoff_frequency_for_coordinate_values=-1, @@ -49,7 +49,7 @@ def __init__(self, session_dir, trial_name, leg='auto', self.coordinateValues = self.get_coordinate_values() # Find what leg is moving. - self.leg, self.contLeg = self.get_y_balance_leg() + self.leg = self.get_y_balance_leg() # Define reference frame. self.R_world_to_yBalance = self.compute_y_balance_frame() @@ -77,8 +77,8 @@ def compute_y_balance_frame(self): # y up, x-> line between calc and 2metatarsal head projected onto xz plane, # z -> cross(x,y) - foot = self.markerDict['markers'][self.leg + '_toe_study'] - \ - self.markerDict['markers'][self.leg + '_calc_study'] + foot = self.markerDict['markers'][self.leg['stance'] + '_toe_study'] - \ + self.markerDict['markers'][self.leg['stance'] + '_calc_study'] x = np.mean(foot[:10,:] / np.linalg.norm(foot[:10,:],axis=1,keepdims=True)) x[:,1] = 0 @@ -96,7 +96,14 @@ def segment_y_balance(self): todo=1 - + # rotate marker data (make fxn in utils) + + # subtract stance toe from swing toe + + # Segment start (march back from forward max, forward max, + # 1st diag start, max + # 2nd diag start, max + # return distance and ind