JOINT TORQUE ESTIMATION MODEL OF SEMG SIGNAL FOR ARM REHABILITATION DEVICE USING ARTIFICIAL NEURAL NETWORK TECHNIQUES

Rehabilitation device is used as an exoskeleton for peoples who had failure of their limb. Arm rehabilitation device may help the rehab program to who suffered with arm disability. The device is used to facilitate the tasks of theprogram and minimize the mental effort of the user. Electromyography (EMG) is the techniques to analyze the presence ofelectrical activity in musculoskeletal systems. The electrical activity in muscles of disable person is failed to contract themuscle for movements. To minimize the used of mental forced for disable patients, the rehabilitation device can be utilize by analyzing the surface EMG signal of normal people that can be implemented to the device. The objective of this work is to model the muscle EMG signal to torque for a motor control of the arm rehabilitation device using Artificial Neural Network (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) to model the EMG signal to torque value. The performance result of the network is measured based on the Mean Squared Error (MSE) of the training data and Regression (R) between the target outputs and the network outputs. The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control

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

This paper illustrates the Artificial Neural Network (ANN) technique to estimate 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. Arm rehabilitation device may help the rehab program to whom suffered with arm disability. The device used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. 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. To prevent the muscles from paralysis becomes spasticity the force of movements should minimize the mental efforts. Besides that, in order to minimize the used of mental forced for disable patients, the rehabilitation device can be utilize by analyzing the surface EMG signal of normal people that can be implemented to the device. The objective of this work is to model the muscle EMG signal to torque for a motor control of the arm rehabilitation device 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) to model the EMG signal to torque value. The experimental results show that ANN can well represent EMG-torque relationship for arm rehabilitation device control

Comparative Study of EMG based Joint Torque Estimation ANN Models for Arm Rehabilitation Device

Rehabilitation device is used as an exoskeleton for people who had failure of their limb. Arm rehabilitation device may help the rehab program whom suffered with arm disability. The device used to facilitate the tasks of the program should improve the electrical activity in the motor unit and minimize the mental effort of the user. 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 minimize the used of mental forced for disable patients, the rehabilitation device can be utilize by analyzing the surface EMG signal of normal people that can be implemented to the device. The objective of this work is to compare the performance of the joint torque estimation model from the muscle EMG signal to torque for a motor control of the arm rehabilitation device using Artificial Neural Network (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) to model the EMG signal to torque value. The comparison between two ANN models is made to observe the performance difference between these models. The experimental results show that ANN model with double input nodes has a better performance result in term of Mean Squared Error (MSE) and Regression (R) which is crucially important to represent EMG-torque relationship for arm rehabilitation device control