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Electromyograms (EMG)-based hand gesture recognition systems are a promisingtechnology for human/machine interfaces. However, one of their main limitationsis the long calibration time that is typically required to handle new users.The paper discusses and analyses the challenge of cross-subject generalizationthanks to an original dataset containing the EMG signals of 14 human subjectsduring hand gestures. The experimental results show that, though an accurategeneralization based on pooling multiple subjects is hardly achievable, it ispossible to improve the cross-subject estimation by identifying a robustlow-dimensional subspace for multiple subjects and aligning it to a targetsubject. A visualization of the subspace enables us to provide insights for theimprovement of cross-subject generalization with EMG signals.

EMG subspace alignment and visualization for cross-subject hand gesture classification