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.
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.
By Saba A. Farahani, Hung Cao, Amir M. Rahmani
arXiv:2608. 05697v1 Announce Type: cross Abstract: Respiration provides a continuously available window into physiological state and behavior.
By Mohammad Hosseini, Hamed Khatounabadi, Mohammad Fakharzadeh
arXiv:2606. 00752v1 Announce Type: new Abstract: Autonomous Sensory Meridian Response (ASMR) is a somatosensory phenomenon characterized by pleasant tingling sensations and cardiovascular slowing.
By Tushar Das, Daigo Hozaki, Koushlendra Kumar Singh, Hirohito M. Kondo
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.
By Andrei Velichko, Mehmet Tahir Huyut
arXiv:2406. 07147v3 Announce Type: replace-cross Abstract: This study employs cutting-edge wearable monitoring technology to conduct high-precision, high-temporal-resolution (1-second interval) cognitive load assessment on electroencephalogram (EEG) data from the FP1 channel and heart rate variability (HRV) data of secondary vocational students.
By Ling He, Yanxin Chen, Wenqi Wang, Shuting He, Xiaoqiang Hu