用表面肌电解码手部动作，实现截肢者指尖级控制。
ALVI Interface: Towards Full Hand Motion Decoding for Amputees Using sEMG
- 基于VR采集数据，用Transformer模型将肌电信号转为动作
- 20个自由度的关节角度实时重建，相关性达0.8
- 支持截肢者在虚拟环境中精细操控手指，适合康复与假肢研发
我们提出一种基于表面肌电（sEMG）信号的手部运动解码系统。该系统以25 Hz的实时速度重建20个自由度的指关节角度，专为上肢截肢者设计。离线分析显示，预测动作与实际动作的相关性达到0.8。系统采用集成式流程，包含三个核心部分：(1) 基于虚拟现实（VR）的数据采集平台，(2) 用于肌电到运动转换的Transformer模型，(3) 实时校准与反馈模块，称为ALVI Interface。通过8个sEMG传感器和VR训练环境，用户可实现对虚拟手部的精细控制，精确至指关节级别，演示视频见：youtube链接。
原文摘要 · Abstract (English)
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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