MMV based EMG Signal Reconstruction for Remote Rehabilitation

To overcome the limitations of low compression rates and extended reconstruction times in the Single Measurement Vector (SMV) model for electromyography (EMG) signal processing, this paper presents a compression and reconstruction algorithm based on the Multiple Measurement Vector (MMV) model. The EMG signals undergo preprocessing, and the optimal combination of the Discrete Sine Transform (DST) sparse basis and the BPBDMtx measurement matrix is selected. Signal reconstruction is then performed using the Orthogonal Matching Pursuit (MMV-OMP) algorithm. Experimental results show that the proposed MMV-based compression sensing algorithm significantly outperforms the traditional SMV model in terms of reconstruction quality, compression rate, and reconstruction speed. This method offers a novel pathway for efficient EMG signal reconstruction and has broad potential for applications in sensor technology and remote rehabilitation.