Rather than treating human movement as a simple time-series signal, we interpret it as a complex, nonlinear dynamical system. Using state-space reconstruction, trajectory analysis, and stability metrics, we investigate the intrinsic, underlying structure of motion. We quantify dynamical stability, variability, and pattern transitions in gait and other cyclic movements to understand how physical and neurological constraints reshape motor control architecture. This nonlinear framework reveals the profound complexity of human motion, allowing us to detect fundamental dynamical shifts associated with motor impairment and dysfunction.