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Domain Adaptation for sEMG-based Gesture Recognition with Recurrent Neural Networks (1901.06958v2)
Published 21 Jan 2019 in cs.LG, cs.HC, and stat.ML
Abstract: Surface Electromyography (sEMG/EMG) is to record muscles' electrical activity from a restricted area of the skin by using electrodes. The sEMG-based gesture recognition is extremely sensitive of inter-session and inter-subject variances. We propose a model and a deep-learning-based domain adaptation method to approximate the domain shift for recognition accuracy enhancement. Analysis performed on sparse and HighDensity (HD) sEMG public datasets validate that our approach outperforms state-of-the-art methods.