One of the central challenges in the study of human motor control and learning is the degrees-of-freedom problem. Although the dynamical systems approach (DSA) has provided valuable insights into addressing this issue, its application has largely been confined to cyclic or simplified motor movements. To overcome this limitation, the present study employs neural ordinary differential equations (NODEs) to model the time evolution of non-cyclic full-body movements as a low-dimensional latent dynamical system. Given the temporal complexity full-body kinematic chains, baseball pitching was selected as a representative target movement to examine whether DSA could be extended to more complex, ecologically valid human movements. Results of the verification experiment demonstrated that the time evolution of a complex pitching motion could be accurately predicted (R^2 > 0.45) using the NODE-based dynamical model. Notably, approximately 50% of the variance in the latter half of the pitching motion was explained using only the initial ~8% of the temporal sequence, underscoring how subsequent movement evolves from initial conditions according to ODE-defined dynamics in latent space. These findings indicate the potential to extend the DSA to more complex and ecologically valid forms of human movement.

翻译：人体运动控制与学习研究的核心挑战之一是自由度问题。尽管动力学系统方法（DSA）为应对该问题提供了宝贵见解，但其应用主要局限于周期性或简化的运动模式。为突破此局限，本研究采用神经常微分方程（NODEs），将非周期性全身运动的时序演化建模为低维潜空间动力学系统。鉴于全身运动链具有高度时序复杂性，本研究选择棒球投掷动作作为代表性目标运动，以检验DSA能否拓展至更复杂、更具生态效度的人类运动。验证实验结果表明，基于NODE的动力学模型能准确预测复杂投掷动作的时序演化（R^2 > 0.45）。值得注意的是，仅使用时序序列前约8%的数据即可解释投掷动作后半段约50%的方差，这凸显了后续运动如何根据潜空间中常微分方程定义的动力学规律从初始条件演化而来。这些发现表明，将DSA拓展至更复杂且具生态效度的人类运动形式具有可行性。