Static torque-angle relation of human elbow joint estimated with artificial neural network technique.

Abstract

Static relations between elbow joint angle and torque at constant muscle activity in normal volunteers were investigated with the aid of an artificial neural network technique. A subject sat on a chair and moved his upper- and forearm in a horizontal plane at the height of his shoulder. The subject was instructed to maintain the elbow joint at a pre-determined angle. The wrist was then pulled to extend the elbow joint by the gravitational force of a weight hanging from a pulley. Integrated electromyograms (IEMGs), elbow and shoulder joint angles and elbow joint torque were measured. Then the relation among IEMGs, joint angles and torque was modeled with the aid of the artificial neural network, where IEMGs and joint angles were the inputs and torque was the output. After back propagation learning, we presented various combinations of IEMGs, shoulder and elbow joint angles to the model and estimated the elbow joint torque to obtain the torque-angle relation for constant muscle activation. The elbow joint torque increased and then decreased with extension of the elbow joint. This suggests that if the forearm is displaced from an equilibrium point, the torque angle relation would not act like a simple spring. In a view of the musculoskeletal structure of the elbow joint, the relation between the elbow joint angle and the moment arm of the elbow flexor muscles seems to have a dominant effect on the torque-angle relation.