Optimized Approach to Improve Classification of Wrist Movements via Electromyography Signals

Abstract

An optimized approach aiming to improve classification accuracy of wrist movements via electromyography (EMG) signals is presented here. EMG signals of the different types of wrist movements are obtained from the NINAPRO database. Useful features are extracted from the EMG signals via the waveform length method. The developed optimized classification system contains two main modules, known here as (i) optimized neural network module and (ii) movement prediction module. The optimized neural network module is made up of multiple 2-class neural networks. During Stage 1 Classification, a group of neural network (named NNG_S1) is formed after analyzing the sensitivity computed from the training outcomes of each neural network. A new group of neural network (named NNG_S2) is later formed in Stage 2 Classification after initial elimination via Stage 1 Classification. Further analysis is performed via the movement prediction module to predict the final outcome of the classification. The overall average classification accuracy achieved via the optimized classification system is 8.3% higher than the conventional neural network. The results validate that the optimized classification system performs better than the conventional neural network, providing more accurate signals for manipulating of exoskeleton for rehabilitation purposes.