ALVI Interface: Towards Full Hand Motion Decoding for Amputees Using sEMG
This provides a real-time hand motion decoding system for upper limb amputees, though it appears incremental as it builds on existing EMG and transformer-based methods.
The paper tackles the problem of decoding full hand movements for amputees using surface EMG signals, achieving a 0.8 correlation between predicted and actual movements in offline analysis and enabling real-time control at 25 Hz across 20 degrees of freedom.
We present a system for decoding hand movements using surface EMG signals. The interface provides real-time (25 Hz) reconstruction of finger joint angles across 20 degrees of freedom, designed for upper limb amputees. Our offline analysis shows 0.8 correlation between predicted and actual hand movements. The system functions as an integrated pipeline with three key components: (1) a VR-based data collection platform, (2) a transformer-based model for EMG-to-motion transformation, and (3) a real-time calibration and feedback module called ALVI Interface. Using eight sEMG sensors and a VR training environment, users can control their virtual hand down to finger joint movement precision, as demonstrated in our video: youtube link.