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Surface electromyography (sEMG) and high-density sEMG (HD-sEMG) biosignalshave been extensively investigated for myoelectric control of prostheticdevices, neurorobotics, and more recently human-computer interfaces because oftheir capability for hand gesture recognition/prediction in a wearable andnon-invasive manner. High intraday (same-day) performance has been reported.However, the interday performance (separating training and testing days) issubstantially degraded due to the poor generalizability of conventionalapproaches over time, hindering the application of such techniques in real-lifepractices. There are limited recent studies on the feasibility of multi-dayhand gesture recognition. The existing studies face a major challenge: the needfor long sEMG epochs makes the corresponding neural interfaces impractical dueto the induced delay in myoelectric control. This paper proposes a compactViT-based network for multi-day dynamic hand gesture prediction. We tackle themain challenge as the proposed model only relies on very short HD-sEMG signalwindows (i.e., 50 ms, accounting for only one-sixth of the convention forreal-time myoelectric implementation), boosting agility and responsiveness. Ourproposed model can predict 11 dynamic gestures for 20 subjects with an averageaccuracy 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, theproposed model can achieve over 92% accuracy by retraining less than 10% of theparameters for computational efficiency.

ViT-MDHGR: Cross-day Reliability and Agility in Dynamic Hand Gesture Prediction via HD-sEMG Signal Decoding