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Beyond EDA: A Systematic Review of Multimodal Sympathetic Nervous System Arousal Classification for Stress Detection
Journal article   Open access   Peer reviewed

Beyond EDA: A Systematic Review of Multimodal Sympathetic Nervous System Arousal Classification for Stress Detection

Santiago Sosa, Adam K. Fontecchio, Evangelia G. Chrysikou and Jennifer S. Atchison
Sensors (Basel, Switzerland), v 26(5), 1584
03 Mar 2026
DOI: https://doi.org/10.3390/s26051584
PMID: 41829546
Featured in Collection :   Research Supported by Drexel Libraries' OA Programs
url
https://doi.org/10.3390/s26051584View
Published, Version of Record (VoR)Open Access Discount via Drexel Libraries Read and Publish Program 2026CC BY V4.0,  Open

Abstract

multimodal stress detection electrodermal activity nervous system arousal wearable physiological sensing sensor fusion subject-independent modeling deep learning free-living stress assessment affective computing
Electrodermal activity (EDA) is a powerful anchor for assessing human sympathetic nervous system (SNS) arousal. However, EDA alone is only one facet of physiological response. Researchers have increasingly moved away from single-sensor analysis to multimodal wearable systems, integrating EDA with other signals such as heart rate variability (HRV), photoplethysmography (PPG), skin temperature (SKT), blood oxygen (SpO2) and more. This critical shift in methodology is not yet reflected in current reviews of the literature. Existing surveys thoroughly cover EDA as a standalone measure, but the combination of sensor technologies has been largely unexamined. In this context, multimodal refers to integrating EDA with complementary biosignals (HRV, PPG, SKT, SpO2, etc.) commonly captured by modern wearable platforms. This review provides a comprehensive analysis focused on multimodal systems for assessing SNS arousal. A total of 58 studies met the inclusion criteria. We map the landscape, from single signal methods to complex sensor-fusion, and highlight advances in multimodal sensor models, physiological modeling, and context-aware sensing. We also examine recent advances in signal processing and machine learning that enhance multimodal SNS arousal inference, outlining current capabilities and identifying open directions for future work. By providing a framework of this emerging field, this paper serves as a resource for all researchers aiming to build and deploy the next generation of context-aware SNS arousal-sensing technology.

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