Towards an Optimal Model of Post-Stroke Sensorimotor Control

Abstract

After stroke, motor control deficits are a common cause of disability. An optimal control model could provide insight into how motor and cognitive impairments influence post-stroke sensorimotor control. We present an initial attempt at modeling how post-stroke individuals perform a robot-based preview tracking task. Specifically, we fit a feedback controller with added delay and position bias to the impaired and less-impaired limbs of 11 stroke patients. We use an inverse linear quadratic regulator (LQR) to find the cost function for each participant. We also use linear regression to determine relationships between model parameters and clinical assessments of motor and cognitive impairments. Our main findings are that post-stroke sensorimotor control can be modeled as an optimal feedback controller and adding a delay and bias constant may improve the performance of this model. We also comment on how model parameters may be related to clinical assessments via linear regression. Future work should model more individuals and evaluate kinematic performance.