用肌电图构建手势图网络，实时识别准确率达99%。
A Graph Neural Network Model for Real-Time Gesture Recognition Based on sEMG Signals
- 将肌电信号转为肌肉激活图谱，用图神经网络建模
- 平均分类准确率99%，推理时间仅48毫秒
- 适合假肢控制与增强现实等实时场景
为实现先进手部假肢和增强现实的无缝控制，精准及时的手势识别至关重要。表面肌电（sEMG）信号常用于此目的，通过腕部周围电极采集。本文提出一种新方法，利用包含前臂肌肉激活模式信息的图网络表示sEMG信号，并基于该图网络构建了图神经网络机器学习算法，实现实时手势识别。在MyoBand设备（8电极）上对8名健康受试者进行测试，结果表明该方法平均分类准确率达99%，优于现有技术。在M1 Pro CPU上，图构建与预测平均耗时48毫秒，充分满足实时应用需求。
原文摘要 · Abstract (English)
For seemless control of advanced hand prostheses and augmented reality, accurate and immediate hand gestures recognition is essential. Surface electromyography (sEMG) signals obtained from the forearm are commonly employed for this purpose. In this paper, we present a novel approach for sEMG representation that utilizes graph networks which contain information about muscle activation patterns in the forearm. Based on these graph networks, we have developed a machine learning algorithm capable of real-time hand gesture recognition using a graph neural network. The algorithm's performance was evaluated using sEMG signals acquired from myoband, which has 8 electrodes placed around the forearm, involving 8 healthy subjects. The proposed method demonstrated an average classification accuracy of 99\%, surpassing the performance of state-of-the-art techniques. The average time for both graph construction and prediction stood at 48ms utilizing a M1 pro CPU, rendering the approach well-suited for real-time applications.
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