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

TL;DR

This paper proposes a method to improve cross-subject hand gesture classification using EMG signals by identifying and aligning a shared low-dimensional subspace, supported by visualization insights.

Contribution

It introduces an approach to enhance cross-subject EMG-based gesture recognition through subspace alignment and visualization, addressing calibration challenges.

Findings

Aligning a shared subspace improves cross-subject estimation.

Visualization provides insights into EMG signal variability.

Pooling multiple subjects alone does not significantly improve accuracy.

Abstract

Electromyograms (EMG)-based hand gesture recognition systems are a promising technology for human/machine interfaces. However, one of their main limitations is the long calibration time that is typically required to handle new users. The paper discusses and analyses the challenge of cross-subject generalization thanks to an original dataset containing the EMG signals of 14 human subjects during hand gestures. The experimental results show that, though an accurate generalization based on pooling multiple subjects is hardly achievable, it is possible to improve the cross-subject estimation by identifying a robust low-dimensional subspace for multiple subjects and aligning it to a target subject. A visualization of the subspace enables us to provide insights for the improvement of cross-subject generalization with EMG signals.