Electrodermal Activity as a Unimodal Signal for Aerobic Exercise Detection in Wearable Sensors
cs.LG, cs.AI
Submitted: 2026-03-16
Updated: 2026-03-16
Journal ref: Conference Paper: Proceedings of the 2026 IEEE 9th International Conference on Signal & Image Processing Applications (ICSIPA 2026), 09/09/2026, Melaka, Malaysia
License: http://creativecommons.org/licenses/by/4.0/
The gist: Electrodermal Activity (EDA) is a non-invasive physiological signal widely available in wearable devices and reflects sympathetic nervous system (SNS) activation.
Terminology
Abstract
Electrodermal Activity (EDA) is a non-invasive physiological signal widely available in wearable devices and reflects sympathetic nervous system (SNS) activation. Prior multi-modal studies have demonstrated robust performance in distinguishing stress and exercise states when EDA is combined with complementary signals such as heart rate and accelerometry. However, the ability of EDA to independently distinguish sustained aerobic exercise from low-arousal states under subject-independent evaluation remains insufficiently characterized. This study investigates whether features derived exclusively from EDA can reliably differentiate rest from sustained aerobic exercise. Using a publicly available dataset collected from thirty healthy individuals, EDA features were evaluated using benchmark machine learning models with leave-one-subject-out (LOSO) validation. Across models, EDA-only classifiers achieved moderate subject-independent performance, with phasic temporal dynamics and event timing contributing to class separation. Rather than proposing EDA as a replacement for multimodal sensing, this work provides a conservative benchmark of the discriminative power of EDA alone and clarifies its role as a unimodal input for wearable activity-state inference.
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