Mayats-Alpay Liora, Soangra Rahul
Computational and Data Sciences, Schmid College of Science and Technology, Chapman University, Orange 92866, CA, USA.
Crean College of Health and Behavioral Sciences, Fowler School of Engineering Chapman University, Orange 92866, CA, USA.
2023 Int Conf Next Gener Electron NEleX (2023). 2023 Dec;2023. doi: 10.1109/nelex59773.2023.10421398.
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.
人体运动在执行过程中涉及多个肢体之间的复杂协调。人类步态是周期性的，并且膝盖的运动本质上遵循线性模型无法充分捕捉的非线性动态行为。在本研究中，采用了先进的机器学习（ML）技术，结合使用Python的非线性动力学稀疏识别（SINDy）算法来揭示步行过程中膝盖运动的控制方程。我们在正常行走过程中使用红外标记收集了一名受试者的膝盖运动数据。我们利用PySINDy库来确定控制方程，并计算与膝盖运动学相关的动态系统系数。我们的结果强调了步态中动态系统的控制方程，特别是步行过程中的膝盖运动学。我们发现SINDy算法可以有效地揭示运动科学中的非线性动态系统。
