arXiv Machine Learning
Aug 18
ReliaGate: Reliability Routing for Low-Stakes Wearable Stress Prediction
arXiv:2608. 15951v1 Announce Type: new Abstract: We study when a wearable stress system should surface a prediction rather than change it.
When Clean Signals Are Not Enough: Detecting Structural Ambiguity for Safe Wearable Stress Classification
The paper introduces the Individual Conformal Coupling Monitor (ICCM), a lightweight pre‑inference tool that detects structural ambiguity—when physiological signals that appear plausible individually form a pattern poorly supported by a person’s non‑stress baseline—in wearable stress classifiers. On the WESAD dataset, a Random Forest achieves high mean accuracy but fails entirely for Subject 14 due to weakened cross‑signal coupling near stress onset. ICCM quantifies subject‑specific coupling divergence and can route data to classify, defer, or abstain, reducing false positives slightly and withholding some misclassified windows, though it does not fully correct the failure.
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arXiv Machine Learning
Sep 14
State-specific respiratory signatures for affective and stress recognition: Interpretable respiratory markers, autocorrelation lags, and compact CNN models
The study investigates respiratory signals from the WESAD dataset to detect stress and other affective states. It compares compact 1‑D CNN models trained on raw 60‑second signals with handcrafted respiratory signatures that capture timing, variability, waveform, spectral, and autocorrelation features. While the CNN achieves the highest accuracy for stress detection, the handcrafted signatures provide stronger, physiologically interpretable markers for baseline, amusement, and especially meditation states.
arXiv Machine Learning
Jul 10
Unit-Independent Low-Rate Wrist GSR Processing for Stress Detection Using Phasic nSCR Features
arXiv:2607. 08007v1 Announce Type: cross Abstract: Galvanic skin response (GSR) is widely used for stress detection, but wrist-based GSR remains challenging because its absolute amplitude can differ substantially from laboratory-grade palmar measurements.
arXiv Machine Learning
Jun 26
State-Specific Respiratory Signatures for Affective and Stress Recognition: Interpretable Respiratory Markers, Autocorrelation Lags, and Compact CNN Models
arXiv:2606. 26723v1 Announce Type: cross Abstract: Respiratory activity is a direct and interpretable physiological channel for wearable stress and affective-state recognition, yet many studies emphasize classification accuracy without identifying which respiratory properties separate different states.
arXiv AI
Aug 11
CFD-Guided Detection of Concept Drift in Multimodal Physiologic Signals
arXiv:2608. 07759v1 Announce Type: cross Abstract: Cardiovascular AI models can classify clean elec- trocardiogram (ECG) signals, but real wearable signals change because of motion, breathing, posture, sensor contact, and true clinical deterioration.
Hugging Face Trending Papers
Jul 9
Unit-Independent Low-Rate Wrist GSR Processing for Stress Detection Using Phasic nSCR Features
Galvanic skin response (GSR) is widely used for stress detection, but wrist-based GSR remains challenging because its absolute amplitude can differ substantially from laboratory-grade palmar measurements. In this paper, we propose a unit-independent low-rate wrist GSR processing pipeline to extract the number of skin conductance responses per minute (nSCR/min) as a stress-related feature.
arXiv AI
6d ago
When Temporal Perturbations Act Like Sensor Biases: Label-Free Auditing of Wearable Activity Recognizers
The paper introduces SpectrumAudit, a label‑sealed auditing method for wearable human‑activity recognition models that uses phase‑randomized full‑window stimuli to probe sensor biases. By replaying the DC component and a zero‑mean residual on held‑out subjects, the audit demonstrates significant accuracy drops across 27 victim models, with DC perturbations proving more harmful than AC in most cases. The study also shows that the audit can distinguish between persistent sensor offsets and zero‑mean variations under a fixed peak‑budget.
arXiv Machine Learning
Sep 24
When Adaptation Hurts: Split Sensitivity and Person-Level Negative Transfer in Federated Wearable Onboarding
The paper evaluates six onboarding strategies for federated wearable models on five datasets using a leakage‑controlled protocol that fixes source checkpoints and separates calibration from evaluation. Results show that while average accuracy is high, person‑level performance can drop significantly, with some methods causing negative transfer for certain users. The study highlights that mean accuracy alone is insufficient and provides an auditable benchmark and failure map for future development.
arXiv Machine Learning
Sep 22
Interpretable Stress Detection from ECG Signals Using Motif-Based Anomaly Analysis
arXiv:2609.22179v1 Announce Type: cross Abstract: Stress detection using physiological signals has gained significant attention due to its impact on both physical and mental health. While existing ap...
arXiv AI
Sep 7
BioSync: Transformer-Based Cross-Modal Fusion for a Multimodal Physiological Digital Biomarker
BioSync is a transformer-based model that fuses cardiac, neural, behavioral, and speech data from wearables and mobile devices into a continuous composite digital biomarker called the BioSync Index (BSI). The architecture uses multi-head self-attention on modality tokens and a linear branch for feature concatenation, inspired by latent-variable measurement theory. Evaluations on synthetic cohorts for cognitive decline and metabolic-autonomic conditions show BioSync achieving AUCs of 0.928 and 0.764 accuracy/F1 of 0.766, outperforming simple concatenation and other fusion strategies in most corruption scenarios.
arXiv AI
Jul 7
CogAdapt: Adapting Clinical ECG Foundation Models for Wearable Cognitive Load Assessment
arXiv:2605. 22774v3 Announce Type: replace-cross Abstract: Assessing cognitive load continuously and at low latency would help adaptive human-computer interaction, but it remains hard because labeled data are scarce and models generalize poorly across subjects.
arXiv AI
Jul 22
Beyond Output-Space Calibration: Spectral Evidence Bundling for Selective Reliability Estimation in Time-Series Classification
arXiv:2607. 18279v1 Announce Type: cross Abstract: Post-hoc calibration for time-series classification usually remaps output scores, but deployment decisions such as trust, abstention, and review depend on whether a confident prediction is supported by the current temporal signal.