Electromyography (EMG) signal processing for assistive medical device control has been developed for clinical rehabilitation. The accuracy of operation and responsive time are still needed to be optimized. The purpose of this study was to determine and compare the efficiency of different artificial neural network-based machine learning (ML) algorithms in multiple channels surface EMG (sEMG) signal processing. EMG recorded from the forearm was processed for hand motion recognition. Performance of multilayer neural network training function “Trainlm” and “Trainscg” algorithms were evaluated based on their accuracy and duration required for EMG signal processing. The results showed both algorithms processed sEMG signal within less than 100 ms with the accuracy of Trainlm algorithm higher than Trainscg algorithm. The performance of the proposed methods was tested among five healthy subjects with accuracy higher than 98%. These outcomes suggested the Trainlm and Trainscg ML algorithms effectively recognized hand motion patterns, potentially they can be used for volitional control of hand robotic assistive device.
Backpropagation neural network, Signal processing, Electromyography, Machine learning, Hand, Pattern recognition