Predicting EMG Based Elbow Joint Torque Model Using Multiple Input ANN Neurons for Arm Rehabilitation

Mohd Hafiz Jali, Tarmizi Ahmad Izzuddin, Zul Hasrizal Bohari, Mohamad Fani Sulaima, Hafez Sarkawi
2014 2014 UKSim-AMSS 16th International Conference on Computer Modelling and Simulation  
This paper illustrates the Artificial Neural Network (ANN) technique to predict the joint torque estimation model for arm rehabilitation device in a clear manner. This device acts as an exoskeleton for people who had failure of their limb. Electromyography (EMG) is the techniques to analyze the presence of electrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract the muscle for movements. In order to prevent the muscles from
more » ... muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. The objective of this work is to model the muscle EMG signal to torque using ANN technique. The EMG signal is collected from Biceps Brachii muscles to estimate the elbow joint torque. A two layer feed-forward network is trained using Back Propagation Neural Network (BPNN). The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control.
doi:10.1109/uksim.2014.78 dblp:conf/uksim/JaliIBSS14 fatcat:i4dyjviypjcbbiujki4opacf7e