Modelling Of Muscle EMG To Torque By The Neural Network Model Of Backpropagation

Abstract

The application of a approach to solution of the EMG-torque relation in the ankle joint under isometric, supine conditions is addressed in this study. The backpropagation model was used in this analysis to simulate a multilayer perceptron for solution to the muscle EMG-joint torque mapping. Measurements from 6 muscle sites are entered into the model as in the input, while the measured torque is entered into the model as the ideal output, to which the model Cutput is compared. The applied muscle signals may be considered the 'intent' of the system, while joint torque is the 'controlled' variable. It is expected that the results of this study will show feasibility for application of learning algorithms in the controller for intent recognition systems,such as robotic m anipulators, myoelectric prostheses, and functional electrical stimulators. I The application of models as an adaptive modelling technique is the primary focus of this study.The feasibility of designing a neural network which obviates the need to specify the EMG-torque relationship apriori wlll be addressed in this study. The electromyography (EMG) or muscle signal typlifies features of physiologic signals: real-time,non