When One Sensor Fails: Tolerating Dysfunction in Multi-Sensor Prototypes
This addresses reliability issues in human-computer interaction and clinical applications where sensor dysfunction can compromise usability, though it appears incremental as it builds on existing methods.
The paper tackles sensor failure in multi-sensor sEMG systems by proposing a fail-safe framework that ranks sensors based on importance, using rock-paper-scissors gesture data to identify crucial versus replaceable sensors for robust design.
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.