Surface electromyography (sEMG) sensors are widely used in human-computer interaction, yet the failure of a single sensor can compromise system usability. We propose a methodological framework for implementing a fail-safe mechanism in multi-sensor sEMG systems. Using arm sEMG recordings of rock-paper-scissors gestures, we extracted hand-crafted features and quantified class separability via the maximum Fisher discriminant ratio (FDR). A multi-layer perceptron validated our approach, consistent with prior findings and physiological evidence. Systematic sensor ablations and FDR analysis produced a ranking of crucial versus replaceable sensors. This ranking informs robust device design, sensor redundancy, and reliability in clinical and practical applications.
Cite
@article{arxiv.2604.04832,
title = {When One Sensor Fails: Tolerating Dysfunction in Multi-Sensor Prototypes},
author = {Freek Hens and Amirhossein Sadough and Aleksa Bokšan and Mahyar Shahsavari and Mohammad Mahdi Dehshibi},
journal= {arXiv preprint arXiv:2604.04832},
year = {2026}
}
Comments
Submitted to the International Journal of Parallel, Emergent and Distributed Systems