Classification of Functional Motions of Hand for Upper Limb Prosthesis with Surface Electromyography

Abstract

Significance of rehabilitation engineering is gaining popularity with the advancement in technology as more amputees desire to perform day to day tasks. Researchers are proposing designs and devices related to prosthesis which can achieve principle functions. Ideal upper limb prosthesis is one which can mimic actual hand. Control of Electromyography (EMG) based prosthesis is still in primitive stage as large number of channels is required even for the recognition of only few hand gestures. This study presents classification of essential hand movements for dexterous control of upper limb active prosthesis using surface Electromyography (EMG). Forearm muscles were used to detect these signals. Four pairs of surface electrodes were used with one reference electrode. Thus lesser number of channels used as compared to previous studies. Offline analysis was used to figure out classification accuracy. Time domain feature extraction was done in the initial stage with support vector machine (SVM) analysis used for classification in the later stage. Results showed that hand movements were decoded accurately under latencies of 300ms. Five different movements were classified with the average accuracy between 84-90%. Keywords: Electromyography (EMG), Support Vector Machine (SVM), Prosthesis, Gesture Recognition