Identifying the Best Forearm Muscle to Control Soft Robotic Glove System by Using a Single sEMG Channel

Abstract

Soft robotic gloves controlled by surface electromyography signals (sEMG) can be indispensable tool for assisting patients afflicted with hand impairment to perform daily activities and at home rehabilitation. To simplify these devices and make them more practical for usage in daily basis, they must employ minimum number of sEMG channels. Some studies have developed classification algorithms that employ single sEMG channel, but these algorithms need intensive calculations and require sEMG signal with good signal to noise ratio (SNR) which is difficult to get from patients that have neuromuscular diseases. Therefore, a computationally efficient muscle activity detection algorithm with ability to classify two hand movements (hand close and hand open) has been proposed in a previous study (FLA-MSE algorithm) to control the movement of a soft robotic glove system. This algorithm employs a single sEMG channel on the Flexor Carpi Ulnaris (FCU) muscle to detect and classify low SNR muscle activities. In this paper, an investigation has been conducted on a healthy subject to verify if the FCU muscle is the best forearm muscle for locating the single sEMG channel in order to get the better detection and classification performance for the FLA-MSE algorithm. The results have verified that the FCU muscle is the most suitable location to put the sEMG channel compared to other forearm muscles with respect to obtaining optimum performance of the FLA-MSE algorithm.