Identifying inverse human arm dynamics using a robotic testbed

Abstract

We present a method to experimentally identify the inverse dynamics of a human arm. We drive a person's hand with a robot along smooth reaching trajectories while measuring the motion of the shoulder and elbow joints and the force required to move the hand. We fit a model that predicts the shoulder and elbow joint torques required to achieve a desired arm motion. This torque can be supplied by functional electrical stimulation of muscles to control the arm of a person paralyzed by spinal cord injury. Errors in predictions of the joint torques for a subject without spinal cord injury were less than 20% of the maximum torques observed in the identification experiments. In most cases a semiparametric Gaussian process model predicted joint torques with equal or less error than a nonparametric Gaussian process model or a parametric model.