Abstract
Currently, surface electromyography (sEMG) features of the forearm multi-tendon muscles are widely used in gesture recognition, however, there are few investigations on the inherent physiological mechanism of muscle synergies. We aimed to study whether the muscle synergies could be used for gesture recognition. Five healthy participants executed five gestures of daily life (pinch, fist, open hand, grip, and extension) and the sEMG activity was acquired from six forearm muscles. A non-negative matrix factorization (NMF) algorithm was employed to decompose the pre-treated six-channel sEMG data to obtain the muscle synergy matrixes, in which the weights of each muscle channel determined the feature set for hand gesture classification. The results showed that the synergistic features of forearm muscles could be successfully clustered in the feature space, which enabled hand gestures to be recognized with high efficiency. By augmenting the number of participants, the mean recognition rate remained at more than 96% and reflected high robustness. We showed that muscle synergies can be well applied to gesture recognition.
Highlights
Hand motion analysis is one of the most essential topics in rehabilitation for understanding and restoring human motor function, as the hand is very frequently used in our daily lives [1].Generally, hand finger movements are controlled by the skeletal muscle of the forearms.Surface electromyography signals from multi-tendon forearm muscles can reflect the finger movement pattern [2,3], which is useful to finger motion classification applications such as sign language recognition [4,5] or an electromyography (EMG)-driven robotic hand exoskeleton [6].The difference in the muscle contraction pattern that controls finger movements will alter the sEMG characteristic parameters in the time- or frequency- domain
The EMG activity was acquired from six forearm muscles using the surface EMG system (ME6000, The EMG activity was acquired from six forearm muscles using the surface EMG system Mega Electronics Ltd, Kuopio, Finland)
The results indicated that recognition the synergistic clusters of the sample size on gesture classification, we calculated the gesture recognition rate by augmenting differed from each other according to the distances in the feature space
Summary
Hand motion analysis is one of the most essential topics in rehabilitation for understanding and restoring human motor function, as the hand is very frequently used in our daily lives [1]. The recognition of hand gestures is principally based on the myoelectric feature vectors, using characteristic parameters extracted from the corresponding sEMG signals [7]. Jiang et al [26] proposed an algorithm extracting neural control information from sEMG based on the synergy theorem, to drive myoelectric prostheses performing upper limb movements in multiple degrees of freedom [27]. This study extracted the synergistic patterns of there is little research considering hand gesture recognition with the inherent physiological mechanism forearm multi-tendon muscles for gesture classification. This study extracted the synergistic patterns of forearm multi-tendon muscles for daily activities [33] were selected and the sEMG from six muscles corresponding to the assigned gesture classification. Pattern and the support vector machine (SVM) was employed to investigate the feasibility of muscle synergy in the recognition of different finger motions
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