Identifying Inverse Human Arm Dynamics Using a Robotic Testbed

TitleIdentifying Inverse Human Arm Dynamics Using a Robotic Testbed
Publication TypeConference Paper
Year of Publication2014
AuthorsSchearer, E. M., Y. W. Liao, E. Perreault, M. Tresch, W. Memberg, R. Kirsch, and K. M. Lynch
Conference Name2014 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2014)

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.

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