surface-electromyography

Introduces a montage-agnostic encoder for calibration-light cross-user gesture recognition from surface EMG, using shared weights and electrode coordinates to handle variable channel counts and reduce per-user calibration. It outperforms per-user baselines on some datasets and analyzes factors affecting cross-user transfer.

本研究探索了使用单通道sEMG信号结合轻量级机器学习模型对十种手势进行分类的可行性，准确率高达90%。该研究展示了在低成本、低功耗手势识别方面的潜力。