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.

翻译：表面肌电（sEMG）传感器广泛应用于人机交互，但单个传感器的失效可能影响系统的可用性。我们提出了一种在多传感器sEMG系统中实现故障安全机制的方法论框架。利用手臂sEMG记录的石头-剪刀-布手势数据，我们提取了手工特征，并通过最大Fisher判别比（FDR）量化了类别可分性。多层感知机验证了我们的方法，其结果与已有发现和生理学证据一致。系统性的传感器消融实验和FDR分析得出了关键传感器与可替换传感器的排序。这一排序为硬件鲁棒性设计、传感器冗余及临床与实际应用中的可靠性提升提供了指导。