Document Type
Article
Publication Date
12-2023
Abstract
Human movement involves complex coordination between multiple limbs during execution. Human gait is cyclic, and the knee's movement inherently follows nonlinear dynamic behavior that linear models cannot adequately capture. In this study, advanced Machine Learning (ML) techniques were employed to combine the Sparse Identification of Nonlinear Dynamics (SINDy) algorithm using Python to reveal governing equations of knee movement during walking. We gathered a single subject's knee motion data using infrared markers during normal walking. We utilized the PySINDy library to determine the governing equations and calculated the coefficient of dynamical systems associated with knee kinematics. Our results emphasize governing equations of dynamic systems in gait, particularly the knee kinematics during walking. We found that the SINDy algorithms could effectively reveal nonlinear dynamic systems in movement science.
Recommended Citation
Mayats-Alpay L, Soangra R. Exploring Non-linear Dynamical Structure for Knee Kinematics Using Machine Learning. 2023 Int Conf Next Gener Electron NEleX (2023). 2023;2023:10421398. https://doi.org/10.1109/nelex59773.2023.10421398
Copyright
IEEE
Comments
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in 2023 International Conference on Next Generation Electronics (NEleX). This article may not exactly replicate the final published version. The definitive publisher-authenticated version is available online at https://doi.org/10.1109/nelex59773.2023.10421398.