Electrodermal Activity as a Unimodal Signal for Aerobic Exercise Detection in Wearable Sensors

TL;DR

This study evaluates the effectiveness of electrodermal activity (EDA) alone in distinguishing rest from aerobic exercise using machine learning, providing a benchmark for unimodal wearable sensor performance.

Contribution

It offers a conservative benchmark of EDA's discriminative power for activity inference, clarifying its role as a unimodal input in wearable sensors.

Findings

EDA-only classifiers achieved moderate subject-independent performance.

Phasic temporal dynamics and event timing in EDA contributed to class separation.

The study clarifies EDA's role as a unimodal input rather than a replacement for multimodal sensing.

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.