A Closed-Loop Model-Based Control Framework for Human Gait Simulation in Parkinson’s Disease

Human movement is a complex process involving both the central nervous system and the musculoskeletal system. Walking, in particular, represents a sophisticated motor task requiring precise coordination of multiple joints, especially in the lower limbs, along with continuous neuromuscular control. Mathematical modeling plays a key role in describing the dynamics and temporal evolution of gait patterns. In this study, we present the development of a mathematical model and a MATLAB Simulink-based framework for simulating human gait in the sagittal plane during the single-support phase. Specifically, a Proportional Derivative (PD) controller is implemented in conjunction with the compensation of the underlying nonlinear dynamics. Simulations were conducted using data from a healthy control (HC) subject and an age- and gender-matched subject affected by idiopathic Parkinson’s disease (iPD). The results were analyzed and compared with experimental gait data acquired through a motion capture system developed by BTS Bioengineering. Agreement between simulated and experimental trajectories was quantified using the Root Mean Square Error (RMSE). The proposed framework achieved low tracking errors for both HC and iPD conditions, with RMSE values <0.006 m for hip coordinates, <0.09° for trunk motion, and <0.6° for the lower-limb joint angles, confirming the ability of the model to accurately reproduce subject-specific gait dynamics.