Gaitalytics

docs
tests
package

This Python package provides a comprehensive set of tools and advanced algorithms for analyzing 3D motion capture data. It is specifically designed to process gait data stored in c3d format. Prior to utilizing the features of gaitalytics, it is necessary to perform data labeling, modeling, and filtering procedures.

The library’s versatility allows it to be adaptable to various marker sets and modeling algorithms, offering high configurability.

Functionalities

Input

Currently only c3d files are supported. The library provides a function to load a c3d file into a trial object for usage in the library.

Note

future efforts will be made to support other file formats such as trc, mot, sto and mox files.

Event Detection

Method	Description	options
Marker	based on Zenis 2006	height: The height of peaks for events. threshold: The threshold for detecting events. distance: The min distance in frames between events. rel_height: The relative height of peak for events.

Event Detection Check

Method	Description	options
sequence	Checks gait event sequences Heel Strike - Toe off - Heel Strike - Toe off Left - Right - Left - Right

Event Writing

Currently only c3d files are supported. The main usage for this feature is the correction of detected events.

Segmentation

Currently only the segmentation based on gait-events is supported.

Method	Description	options
HS	Segment based on heel strike
TO	Segment based on toe off

Time Normalization

Currently only linear time normalization is supported.

Note

future efforts will be made to support other time normalization methods such as dynamic time warping.

Method	Description	options
linear	Linear time-normalisation

Feature calculation

Method	Description	options
TimeSeriesFeatures	min max mean sd median amplitude
PhaseTimeSeriesFeatures	stand_min stand_max stand_mean stand_sd stand_median stand_amplitude swing_max swing_mean swing_sd swing_median swing_amplitude
SpatialFeatures	step_length [1] stride_length [1]
TemporalFeatures	cycle_duration swing_duration_perc stance_duration_perc step_width [1] cadence [1] single_support_duration_percent [2] double_support_duration_percent [2]

References

[1] J. H. Hollman, E. M. McDade, and R. C. Petersen, “Normative Spatiotemporal Gait Parameters in Older Adults,” Gait Posture, vol. 34, no. 1, pp. 111–118, May 2011, doi: 10.1016/j.gaitpost.2011.03.024.

[2] A. Gouelle and F. Mégrot (2017), “Interpreting spatiotemporal parameters, symmetry, and variability in clinical gait analysis”, Handbook of Human Motion pp. 1-20, Publisher: Springer International Publishing.

Quickstart

Installation

Fast install with anaconda:

conda install gaitalytics -c DartLab-LLUI

Configuration

Gaitalytics can be used with any marker set, which at least includes three or for hip markers (front left/right, back left/right or sacrum) and four foot markers (left heel/toe, right heel/toe).

Additionally markers can be defined on which standard time-series features such as min max mean etc. will be calculated.

All functionalities in the libraries only take points into account which are configured in as specific yaml file.

Minimal requirements would look like this:

# Markers to analyse
analysis:
  markers: # Markers to analyse
    # Left side
    - "LHipAngles"
    - "LKneeAngles"
    - "LAnkleAngles"
    - "LPelvisAngles"
    - "LThoraxAngles"
mapping:
  markers:
    # Foot
    l_heel: "LHEE"
    r_heel: "RHEE"
    l_toe: "LTOE"
    r_toe: "RTOE"

    # Hip
    l_ant_hip: "LASI"
    r_ant_hip: "RASI"
    l_post_hip: "LPSI"
    r_post_hip: "RPSI"
    sacrum: "SACR"

Simple Pipeline

from gaitalytics import api

# Load configuration (yaml file from above)
config = api.load_config("./pig_config.yaml")

# Load trial from c3d file
trial = api.load_c3d_trial("./test_small.c3d", config)

# Detect events
events = api.detect_events(trial, config)
try:
    # detect events (marker based)
    events = api.check_events(events, config)

    # check events
    api.check_events(events, config)

    # write events to c3d in the same file
    api.write_events_to_c3d(trial, events)

    # segment trial to gait cycles. (Events are already existing in the c3d file)
    trial_segmented = api.segment_trial(trial)

    # calculate features
    features = api.calculate_features(trial_segmented, config)

    # save features
    faetures.to_netcdf("features.nc")

    # export segmented trial to netcdf
    api.export_trial(trial_segmented, config, "output.nc")

api.export_trial(trial_segmented, config, "output.c3d")
except ValueError as e:
    print(e)

Documentation

https://python-gaitalytics.readthedocs.org