  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.
