A Computational Model of Human-Robot Load Sharing during Robot-Assisted Arm Movement Training after Stroke

Abstract

An important goal in robot-assisted movement therapy after neurologic injury is to provide an optimal amount of mechanical assistance to patients as they complete motor tasks. This paper presents a computational model of how humans interact with robotic therapy devices for the task of lifting a load to a desired height. The model predicts that an adaptive robotic therapy device will take over performance of the lifting task if the human motor control system contains a slacking term (i.e. a term that tries to the reduce force output of the arm when error is small) but the robot does not. We present experimental data from people with a chronic stroke as they train with a robotic arm orthosis that confirms this prediction. We also show that incorporating a slacking term into the robot overcomes this problem, increasing load sharing by the patient while still keeping kinematic errors small. These results provide insight into the computational mechanisms of human motor adaptation during rehabilitation therapy, and provide a framework for optimizing robot-assisted therapy.