Robustness-enhanced Myoelectric Control with GAN-based Open-set Recognition
Journal:
arXiv
Published Date:
Dec 20, 2024
Abstract
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.
