Abstract
To enhance the performance of surface electromyography (sEMG)-based gesture recognition, we propose a novel network-agnostic two-stage training scheme, called sEMGPoseMIM, that produces trial-invariant representations to be aligned with corresponding hand movements via cross-modal knowledge distillation. In the first stage, an sEMG encoder is trained via cross-trial mutual information maximization using the sEMG sequences sampled from the same time step but different trials in a contrastive learning manner. In the second stage, the learned sEMG encoder is fine-tuned with the supervision of gesture and hand movements in a knowledge-distillation manner. In addition, we propose a novel network called sEMGXCM as the sEMG encoder. Comprehensive experiments on seven sparse multichannel sEMG databases are conducted to demonstrate the effectiveness of the training scheme sEMGPoseMIM and the network sEMGXCM, which achieves an average improvement of +1.3% on the sparse multichannel sEMG databases compared to the existing methods. Furthermore, the comparison between training sEMGXCM and other existing networks from scratch shows that sEMGXCM outperforms the others by an average of +1.5%.