When One Sensor Fails: Tolerating Dysfunction in Multi-Sensor Prototypes

2026-04-06

AI Summary

This study addresses the significant performance degradation in multi-sensor surface electromyography (sEMG) systems caused by the failure of individual sensors. To enhance system robustness, the authors propose a fault-tolerant framework based on the maximum Fisher Discriminant Ratio (FDR). By combining handcrafted feature extraction with FDR analysis, the approach ranks sensor importance and quantifies each channel’s contribution to a rock–paper–scissors gesture recognition task through systematic sensor ablation experiments using a multilayer perceptron classifier. The method effectively distinguishes between critical and replaceable sensors, thereby demonstrating the feasibility of the proposed fault-tolerant mechanism. These findings offer valuable theoretical support for designing redundancy strategies in high-robustness sEMG devices and inform their clinical deployment.

Technology Category

• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Adversarial Learning & Robustness
• Natural Language Processing: Safety and Robustness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Large-scale security measurements
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics

Abstract

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.

Problem

• sensor failure
• surface electromyography
• multi-sensor system
• system reliability
• fault tolerance

Innovation

• fail-safe mechanism
• sensor ablation
• Fisher discriminant ratio
• multi-sensor redundancy
• sEMG robustness