用掩码建模实现sEMG信号的低延迟稳定意图识别
ReactEMG: Stable, Low-Latency Intent Detection from sEMG via Masked Modeling
- 将意图检测转为实时分段，每帧预测手势
- 零样本条件下达到最先进性能，响应快且无抖动
- 适合可穿戴机器人与假肢系统，无需繁琐校准
表面肌电（sEMG）信号在人机接口中具有潜力，尤其适用于康复和假肢控制。但现有系统难以同时实现快速响应、输出稳定无闪烁，并跨受试者通用而不需耗时校准。本文提出一种基于sEMG的意图检测框架，将连续信号流的意图识别转化为逐时刻分割任务，在手势进行时实时标注。引入掩码建模训练策略，使肌肉活动与用户意图对齐，实现快速启动检测和持续手势稳定追踪。在零样本条件下，对比基线方法，本方法在准确率、延迟和稳定性指标上均达到最优，展现出在可穿戴机器人和下一代假肢系统中的应用前景。项目网站、视频、代码和数据集见：https://reactemg.github.io/
原文摘要 · Abstract (English)
Surface electromyography (sEMG) signals show promise for effective human-machine interfaces, particularly in rehabilitation and prosthetics. However, challenges remain in developing systems that respond quickly to user intent, produce stable flicker-free output suitable for device control, and work across different subjects without time-consuming calibration. In this work, we propose a framework for EMG-based intent detection that addresses these challenges. We cast intent detection as per-timestep segmentation of continuous sEMG streams, assigning labels as gestures unfold in real time. We introduce a masked modeling training strategy that aligns muscle activations with their corresponding user intents, enabling rapid onset detection and stable tracking of ongoing gestures. In evaluations against baseline methods, using metrics that capture accuracy, latency and stability for device control, our approach achieves state-of-the-art performance in zero-shot conditions. These results demonstrate its potential for wearable robotics and next-generation prosthetic systems. Our project website, video, code, and dataset are available at: https://reactemg.github.io/
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