prosthetic-control

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.

本文介绍了EMG-CrossFormer，一种用于多模态sEMG手部手势识别的混合卷积-Transformer模型，通过交叉注意力融合sEMG和惯性信号，在NinaPro数据集上达到了最先进的准确率。