发表机构
University of California, Irvine（加利福尼亚大学欧文分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对可穿戴压力分类器对特定个体失效的问题，提出ICCM监测器量化耦合偏差，实现分类、推迟或弃权，实验显示其可作为生理信号不匹配的可解释指标。
AI 中文摘要
可穿戴设备压力分类器虽能达到较高的平均性能，但对特定个体可能完全失效。在WESAD数据集上，随机森林（Random Forest）的平均准确率达93.0%，但对14号受试者的F1值为0，该受试者在压力发作附近的跨信号耦合减弱。我们将此现象称为结构歧义：个体层面看似合理的生理通道会形成一种信号间模式，而该模式与该受试者的非压力参考信号匹配度较差。我们提出了个体保形耦合监测器（Individual Conformal Coupling Monitor, ICCM），这是一种轻量且可解释的推理前监测器，可量化受试者特定的耦合偏差，并将每个信号窗口路由至分类、推迟或弃权（不执行），无需对下游分类器进行重新训练。在WESAD（N=15）和Stress-Predict（N=35）数据集上，全队列的歧义与准确率之间的皮尔逊相关系数为负（r=-0.607，p=0.016；r=-0.412，p=0.014）。鲁棒性分析对该结果有所修正：秩相关系数不显著，且移除14号受试者后WESAD的相关性消失。ICCM将假阳性数量从29降至27、从94降至92，但配对变化均不显著；它对14号受试者的21个压力窗口中的3个进行了推迟处理，但未修复漏检压力的失效问题。这些结果表明，ICCM可作为一种可解释的生理信号不匹配及个体失效的指标，而非独立的安全保障。
英文摘要
Wearable stress classifiers can achieve strong average performance while failing completely for a particular individual. On WESAD, a Random Forest reaches 93.0% mean accuracy yet yields F1 = 0 for Subject 14, whose cross-signal coupling weakens near stress onset. We call this structural ambiguity: individually plausible physiological channels form an inter-signal pattern that is poorly supported by the person's non-stress reference. We introduce the Individual Conformal Coupling Monitor (ICCM), a lightweight and transparent pre-inference monitor that quantifies subject-specific coupling divergence and routes each window to classify, defer, or abstain without retraining the downstream classifier. Across WESAD (N = 15) and Stress-Predict (N = 35), full-cohort Pearson associations between ambiguity and accuracy are negative (r = -0.607, p = 0.016; r = -0.412, p = 0.014). Robustness analyses temper this finding: rank correlations are not significant, and the WESAD association disappears when Subject 14 is removed. ICCM changes false-positive counts from 29 to 27 and 94 to 92, although neither paired change is significant. It withholds 3 of Subject 14's 21 stress windows but does not repair the missed-stress failure. These results position ICCM as an interpretable signal of unsupported physiology and individual failure, rather than a stand-alone safety guarantee.
Comments4 pages, 3 figures, 1 table. Accepted at the 2026 IEEE 22nd International Conference on Body Sensor Networks (BSN 2026)
