  Surface electromyography (sEMG) and high-density sEMG (HD-sEMG) biosignals
have been extensively investigated for myoelectric control of prosthetic
devices, neurorobotics, and more recently human-computer interfaces because of
their capability for hand gesture recognition/prediction in a wearable and
non-invasive manner. High intraday (same-day) performance has been reported.
However, the interday performance (separating training and testing days) is
substantially degraded due to the poor generalizability of conventional
approaches over time, hindering the application of such techniques in real-life
practices. There are limited recent studies on the feasibility of multi-day
hand gesture recognition. The existing studies face a major challenge: the need
for long sEMG epochs makes the corresponding neural interfaces impractical due
to the induced delay in myoelectric control. This paper proposes a compact
ViT-based network for multi-day dynamic hand gesture prediction. We tackle the
main challenge as the proposed model only relies on very short HD-sEMG signal
windows (i.e., 50 ms, accounting for only one-sixth of the convention for
real-time myoelectric implementation), boosting agility and responsiveness. Our
proposed model can predict 11 dynamic gestures for 20 subjects with an average
accuracy of over 71% on the testing day, 3-25 days after training. Moreover,
when calibrated on just a small portion of data from the testing day, the
proposed model can achieve over 92% accuracy by retraining less than 10% of the
parameters for computational efficiency.
