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.

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In this paper, we use four basic morphological operators to construct a bandpass filter, and also investigate the construct method of the filter in detail, Furthermore, we give the algorithm flowchart of this filter. The result of experiment shows that this morphological filter can be applied to eliminate noise from EEG signal by two different length structure elements and it has advanced property that can remove noise without damage signal useful information. Using this algorithm, we can filter noise and extract spiky transients in EEG effectively.