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arXiv · 2502.21256

ALVI Interface: Towards Full Hand Motion Decoding for Amputees Using sEMG

Aleksandr Kovalev·Anna Makarova·Petr Chizhov·Matvey Antonov·Gleb Duplin·Vladislav Lomtev·Viacheslav Gostevskii·Vladimir Bessonov·Andrey Tsurkan·Mikhail Korobok·Aleksejs Timčenko
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Abstract

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.

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Aleksandr Kovalev, Anna Makarova, Petr Chizhov, Matvey Antonov, Gleb Duplin, Vladislav Lomtev, Viacheslav Gostevskii, Vladimir Bessonov, Andrey Tsurkan, Mikhail Korobok, Aleksejs Timčenko. 2025-02-28. ALVI Interface: Towards Full Hand Motion Decoding for Amputees Using sEMG. https://arxiv.org/abs/2502.21256

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