Defining Experimental Design for Human Motor Control Identification: A Novel Framework

Abstract

Characterizing each person’s sensorimotor profile is crucial for designing precise and personalized motor rehabilitation therapies. Building on our previous work in system identification of human motor control dynamics, we now extend our parameter recovery technique developed in synthetic models to a real-world human experiment. This twin-based digital method actively guides the experimental design by selecting the most informative perturbations and movement conditions to most accurately identify (recover) sensory feedback gains. We applied this framework to 10 neurotypical participants, analyzing their performance during arm planar reaching movements. By combining the optimized experimental design with this forward–inverse modeling pipeline, we estimated individual sensory feedback gains. These gains were then used to simulate movement trajectories, achieving a movement prediction accuracy of 85% compared to withheld trajectories performed by the same subjects. These results validate the ability of our mathematical model to capture and explain individual sensorimotor dynamics through the identification of subject-specific feedback gains. This approach offers a promising tool for gaining insights into the roles of different sensory channels and identifying the most informative data required for efficient assessment.