用生成对抗网络提升肌电控制对未知动作的鲁棒性
Robustness-enhanced Myoelectric Control with GAN-based Open-set Recognition
- 用GAN判别器识别并拒收未知动作，防止误分类
- 已知动作识别准确率达97.6%，误报率降低23.6%
- 轻量设计适合部署在边缘设备，实用性强
肌电信号广泛应用于人体运动识别与康复医疗，但其变异性和易受噪声干扰严重制约了肌电控制系统可靠性。现有识别算法难以有效应对未知动作，导致系统不稳定与错误频发。本文提出一种基于生成对抗网络（GAN）的新框架，通过开放集识别增强肌电控制的鲁棒性与可用性。该方法利用GAN判别器识别并拒绝未知动作，从而维持系统稳定性。在公开数据集与自采数据上的实验表明，已知动作识别准确率达97.6%，拒收未知动作后主动误报率（AER）改善23.6%。所提方法计算高效，适用于边缘设备部署，具备实际应用价值。
原文摘要 · Abstract (English)
Electromyography (EMG) signals are widely used in human motion recognition and medical rehabilitation, yet their variability and susceptibility to noise significantly limit the reliability of myoelectric control systems. Existing recognition algorithms often fail to handle unfamiliar actions effectively, leading to system instability and errors. This paper proposes a novel framework based on Generative Adversarial Networks (GANs) to enhance the robustness and usability of myoelectric control systems by enabling open-set recognition. The method incorporates a GAN-based discriminator to identify and reject unknown actions, maintaining system stability by preventing misclassifications. Experimental evaluations on publicly available and self-collected datasets demonstrate a recognition accuracy of 97.6\% for known actions and a 23.6\% improvement in Active Error Rate (AER) after rejecting unknown actions. The proposed approach is computationally efficient and suitable for deployment on edge devices, making it practical for real-world applications.
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