Hill-Based Model as a Myoprocessor for a Neural Controlled Powered Exoskeleton Arm - Parameters Optimization

Abstract

The exoskeleton robot, serving as an assistive device worn by the human (orthotic), functions as a human amplifier. Setting the human machine interface (HMI) at the neuro-muscular level may lead to seamless integration and an intuitive control of the exoskeleton arm as a natural extension of the human body. At the core of the exoskeleton HMI there is a myoprocessor. It is a model of the human muscle, running in real-time and in parallel to the physiological muscle, that predicts joint torque as a function of the joint kinematics and
neural activation levels. The study is focused on developing a myoprocessor based on the Hill phenomenological muscle model. Genetic algorithms were used to optimize model internal parameters using an experimental database that provides inputs to the model and allows for performance assessment. The results indicate high correlation between joint moment predictions of the model and the measured data. Consequently, the myoprocessor seems an adequate model, sufciently robust for further integration into the exoskeleton control system.

Exoskeleton Prototype 3

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Cavallaro E., J. Rosen, J. C. Perry, S. Burns, B. Hannaford, Hill Based Model as a Myoprocessor for a Neural Controlled Powered Exoskeleton Arm – Parameter Optimization, Proceedings of the 2005 IEEE international Conference on Robotics and Automation, ICRA 2005, pp. 4525 – 4530, Barcelona Spain, April 2005 [CP18 - BRL]

Cavallaro E., J. Rosen, J. C. Perry, S. Burns, Myoprocessor for Neural Controlled Powered Exoskeleton Arm, IEEE Transactions on Biomedical Engineering, pp. 2387-2396, Vol. 53, No. 11, November 2006 [JP 12]