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.

翻译：表面肌电信号（sEMG）与高密度表面肌电信号（HD-sEMG）因其能以可穿戴无创方式进行手势识别/预测，已被广泛应用于假肢设备的肌电控制、神经机器人以及新兴的人机交互领域。研究表明此类方法在日内（同一天）预测中表现出色。然而，由于传统方法随时间推移的泛化能力较差，其隔天（训练日与测试日分离）性能会显著下降，从而阻碍了该技术在实际场景中的应用。目前关于多天手势识别可行性的研究十分有限。现有研究面临一项主要挑战：较长的肌电信号时间窗要求会导致肌电控制延迟，使得相应的神经接口缺乏实用性。本文提出了一种基于紧凑型ViT网络的多天动态手势预测方法。该模型仅依赖极短的HD-sEMG信号窗口（即50毫秒，仅为实时肌电实现常规时长的六分之一），从而显著提升了敏捷性与响应速度，成功应对了上述挑战。所提模型能对20名受试者的11种动态手势进行预测，在训练后3至25天的测试日上平均准确率超过71%。此外，当仅利用测试日少量数据进行标定后，该模型通过重新训练不足10%的参数以实现计算效率优化，准确率可超过92%。