EMG subspace alignment and visualization for cross-subject hand gesture
  classification
Martin Colot Cédric Simar Mathieu Petieau Ana Maria Cebolla Alvarez Guy Cheron Gianluca Bontempi
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.