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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.
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.
Method | Description | options |
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Marker | based on Zenis 2006 |
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Method | Description | options |
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sequence |
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Currently only c3d files are supported. The main usage for this feature is the correction of detected events.
Currently only the segmentation based on gait-events is supported.
Method | Description | options |
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HS | Segment based on heel strike | |
TO | Segment based on toe off |
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 |
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linear | Linear time-normalisation |
Method | Description | options |
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TimeSeriesFeatures |
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PhaseTimeSeriesFeatures |
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SpatialFeatures |
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TemporalFeatures |
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[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.
Fast install with anaconda:
conda install gaitalytics -c DartLab-LLUI
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"
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:
# check events
api.check_events(events)
# write events to c3d in the same file
api.write_events_to_c3d("./test_small.c3d", events, './test.c3d')
# add events to trial
trial.events = 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)
# normalise time
trial_normalized = api.time_normalise_trial(trial_segmented)
# save features
features.to_netcdf("features.nc")
# export segmented trial to netcdf
api.export_trial(trial_segmented, "output_segments")
api.export_trial(trial_normalized, "output_norm")
except ValueError as e:
print(e)