Learning Nonlinear Motor Control: How Integrating Machine Learning and Nonlinear Dynamics Reveals Structure, Adaptation, and Control in Human Movement

Human movement emerges from complex interactions between neural processes, musculoskeletal dynamics, and environmental constraints, resulting in behavior that is inherently nonlinear. Therefore, nonlinear dynamical systems approaches have been widely used to characterize variability, stability, and coordination in motor behavior. However, despite their conceptual value, these methods are often applied post hoc and remain limited in their ability to support prediction, control, and integration of high-dimensional multimodal data. Artificial intelligence (AI) provides a complementary modeling framework capable of addressing these limitations. Yet many current AI applications treat motor signals primarily as feature sets for classification or regression, leaving the underlying dynamical structure of movement underexplored. This review synthesizes recent research that integrates AI with nonlinear motor control analysis to model, interpret, and control human movement across neural, biomechanical, and behavioral domains. We organize related studies according to the type of nonlinear motor control problem addressed, including input–output mappings, temporal dynamics, and adaptive control policies under conditions of partial observability and nonstationarity. Across these examples, we show that AI becomes scientifically informative when constrained and evaluated by nonlinear dynamical constructs such as attractors, phase relationships, manifolds, and stability structures. Finally, we discuss current limitations and outline future directions toward theory-informed, explainable, and closed-loop AI models for motor control and human–actuator interaction.