Paperdoi:10.3390/s26206425
Trustworthy Stress Detection from Wearable Sensor Signals for Preventive Anxiety Monitoring: Subject-Independent Accuracy and Calibrated Confidence with the Autonomic-Branch Quantum-Graph Network
Sensors 26(20):6425 (2026-10-12)
Key results
All results- AB-QGN (fused)Abstract (Results) (source, opens in a new tab)
84.0
Macro F1 (%) ↑
Stress / affect · WESAD
fused physiological model
Results as reported by the authors; not independently reproduced. A SOTA star marks the best value among results reported under the same evaluation protocol; ties go to the earlier publication.
TL;DR
Uses one fixed model (Autonomic-Branch Quantum-Graph Network: each autonomic branch mapped to a qubit of a shallow entangling circuit) to measure how much the evaluation protocol drives reported stress accuracy. Inputs are HRV and EDA features. Leaky protocols inflate single-modality macro-F1 by 27 to 61 points.
- Form factor: other
Abstract
Wearable monitoring of autonomic dysregulation preceding anxiety disorders must generalize to unseen wearers; reported accuracy reflects the evaluation protocol as much as the model. We measured it with one fixed instrument, the Autonomic-Branch Quantum-Graph Network (AB-QGN), mapping each autonomic branch onto one named qubit of a shallow entangling circuit. With architecture and features fixed, heart-rate variability (HRV) and electrodermal (EDA) features from WESAD and SWELL-KW went through leakage-free subject-grouped and leave-one-subject-out (LOSO) protocols and two leaky ones: single-modality configurations inflated by 27–61 macro-F1 points. Honest fused macro-F1 was 86.2 (WESAD) and 61.4 (SWELL-KW), 84.0 and 55.1 under LOSO; that distance from near-ceiling figures is the result, not a shortfall. Leakage corrupted confidence: conventional calibration left a gradient-boosting baseline at 25.6% expected calibration error, 5.4% after subject-disjoint refitting. Over structure- and parameter-matched classical twins, the quantum core added 3–12 session-level points on SWELL-KW at every seed, not significant at 51 sessions, and none on WESAD when parameter-matched. On a third corpus, AffectiveROAD, honest performance is at chance per window, while window-overlap leakage lifts tree ensembles to 90–98 macro-F1. We claim no advantage over fixed-configuration tree ensembles: the contribution is an auditable encoder and the protocol needed to trust a number.
Verbatim from the publisher's DOI record (opens in a new tab) · Publisher licence: https://creativecommons.org/licenses/by/4.0/
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APA
Papamentzelopoulos, S., & Nikoletseas, S. (2026). Trustworthy Stress Detection from Wearable Sensor Signals for Preventive Anxiety Monitoring: Subject-Independent Accuracy and Calibrated Confidence with the Autonomic-Branch Quantum-Graph Network. Sensors. https://doi.org/10.3390/s26206425
BibTeX
@article{papamentzelopoulos2026trustworthy,
title = {{Trustworthy Stress Detection from Wearable Sensor Signals for Preventive Anxiety Monitoring: Subject-Independent Accuracy and Calibrated Confidence with the Autonomic-Branch Quantum-Graph Network}},
author = {Papamentzelopoulos, Spyridon and Nikoletseas, Sotirios},
year = {2026},
journal = {Sensors},
doi = {10.3390/s26206425}
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