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Calculation of hip joint angles from wearable inertial sensors and optical motion capture.

Project description


pykinematics is an open-source Python package for estimating hip kinematics using both novel Magnetic and Inertial Measurement Unit (MIMU) wearable sensors and existing Optical Motion Capture (OMC) algorithms. The novel MIMU algorithms have been validated against OMC, and include novel methods for estimating sensor-to-sensor relative orientation and sensor-to-segment alignment.


Documentation including the below examples, and the API reference can be found at pykinematics documentation


  • Python >=3.6
  • Numpy
  • Scipy
  • h5py*

pip should automatically collect any uninstalled dependencies.

* h5py is required to run the example code in /scripts/, as the sample data provided (see Example Usage) is stored in the .hdf format. Pip will not catch and install h5py as it is not used by pykinematics, and must be installed manually to run the example code.

pip install h5py

or if using Anaconda

conda install -c anaconda h5py


pykinematics can be installed using pip:

pip install pykinematics

Alternatively, you can clone this repository and install from source.

pykinematics can be uninstalled by running

pip uninstall pykinematics

Running tests

Tests are implemented with pytest, and can be automatically run with:

pytest --pyargs pykinematics.tests

Optionally add -v to increase verbosity.

If you don't want to run the integration tests (methods tests), use the following:

python -m pykinematics.tests --no-integration

If you want to see coverage, the following can be run (assuming coverage is installed):

coverage run -m pytest --pyargs pykinematics.tests
# generate the report
coverage report
# generate a HTML report under ./build/index.html
coverage html

Example Usage

A full example script can be found in /scripts/ This requires a sample data file, which can be downloaded from Sample Data contains a helper function to load the data into Python. Once the data is imported, the bulk of the processing is simple:

import pykinematics as pk

static_calibration_data, star_calibration_data, walk_fast_data = <loaded sample data>

# define some additional keyword arguments for optimizations and orientation estimation
filt_vals = {'Angular acceleration': (2, 12)}

ka_kwargs = {'opt_kwargs': {'method': 'trf', 'loss': 'arctan'}}
jc_kwargs = dict(method='SAC', mask_input=True, min_samples=1500, opt_kwargs=dict(loss='arctan'), mask_data='gyr')
orient_kwargs = dict(error_factor=5e-8, c=0.003, N=64, sigma_g=1e-3, sigma_a=6e-3)

mimu_estimator = pk.ImuAngles(gravity_value=9.8404, filter_values=filt_vals, joint_center_kwargs=jc_kwargs,
                              orientation_kwargs=orient_kwargs, knee_axis_kwargs=ka_kwargs)

# calibrate the estimator based on Static and Star Calibration tasks
mimu_estimator.calibrate(static_calibration_data, star_calibration_data)

# compute the hip joint angles for the Fast Walking on a treadmill
left_hip_angles, right_hip_angles = mimu_estimator.estimate(walk_fast_data, return_orientation=False)

Right hip angles from the sample data for walking fast:

Sample right hip angles

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