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arXiv · 2608.15951

ReliaGate: Reliability Routing for Low-Stakes Wearable Stress Prediction

Jaden Moon·Yu Wu·Arvind Pillai·Andrew Campbell
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Abstract

We study when a wearable stress system should surface a prediction rather than change it. In low-stakes reflection and summary settings, aggregate accuracy is insufficient because withholding can reduce error while leaving some people with little or no information. We formulate fixed-label reliability routing: after a locked classifier emits a protocol-defined stress/non-stress label, a post-hoc gate surfaces that unchanged label or withholds it as unavailable. ReliaGate assembles established confidence, signal-quality/trust, agreement, train-standardized atypicality, and train-fitted geometry cues into a post-hoc correctness score. We evaluate four wearable datasets using subject-disjoint folds, validation-selected routing, paired held-out-subject intervals, and pooled and per-subject analyses. WESAD point estimates favored ReliaGate, UBFC-Phys primary coverage/risk intervals favored ReliaGate, and E4 checks were mixed. ReliaGate provides an operational framework for studying surfaced-label error, output availability, and accepted-output distribution across subjects, without revising labels or providing clinical or finite-sample risk guarantees.

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Jaden Moon, Yu Wu, Arvind Pillai, Andrew Campbell. 2026-08-16. ReliaGate: Reliability Routing for Low-Stakes Wearable Stress Prediction. https://arxiv.org/abs/2608.15951

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