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Joint kinematics estimation using a multi-body kinematics optimisation and an extended Kalman filter, and embedding a
Vincent Bonnet1, Vincent Richard2, Valentina Camomilla3
1LISSI, University of Paris-Est-Créteil, France.
Journal of Biomechanics
|May 29, 2017
Summary
This study evaluated methods to reduce soft tissue artefact (STA) in skeletal movement analysis. Embedding a STA model in extended Kalman filters (EKF) improved joint kinematics accuracy during running.
Area of Science:
- Biomechanics
- Motion Analysis
- Computational Modeling
Background:
- Soft tissue artefact (STA) significantly impacts skeletal movement estimation from stereophotogrammetry.
- Multi-body kinematics optimisation (MKO) and extended Kalman filters (EKF) are proposed to mitigate STA.
- Assessing STA models integrated with these methods is crucial for accurate kinematic analysis.
Purpose of the Study:
- To evaluate the feasibility and efficiency of MKO and EKF when incorporating a mathematical STA model.
- To simultaneously estimate joint kinematics and STA model parameters.
- To compare the performance of MKO and EKF with and without an embedded STA model.
Main Methods:
- Implemented MKO and EKF with and without an integrated STA model.
- Utilized intra-cortical pin and skin markers on thigh, shank, and foot during running.
- Employed a STA model estimating artefact based on adjacent joint kinematics.
Main Results:
- Embedding the STA model reduced marker tracking RMS error in MKO (12.6 to 1.6mm) and EKF (4.3 to 1.9mm).
- MKO with STA model slightly increased RMS difference in joint kinematics (2.0 to 2.1deg).
- EKF with STA model reduced RMS difference in joint kinematics (2.0 to 1.7deg), indicating improved accuracy.
Conclusions:
- Trial-specific calibration of STA models is feasible with MKO and EKF.
- EKF with an embedded STA model shows superior potential for attenuating STA effects.
- EKF with STA model integration enhances the accuracy of estimated joint kinematics in biomechanical analysis.
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