Robustness-enhanced Myoelectric Control with GAN-based Open-set Recognition
This work addresses the need for more robust and stable myoelectric control systems in human motion recognition and medical rehabilitation, representing a domain-specific incremental improvement.
The paper tackled the problem of unreliable myoelectric control systems due to variability and noise in EMG signals by proposing a GAN-based framework for open-set recognition, achieving 97.6% accuracy for known actions and a 23.6% improvement in Active Error Rate after rejecting unknown actions.
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.