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

Area of Science:

• Physiological monitoring
• Wearable technology
• Human-computer interaction

Background:

• Electrodermal activity (EDA) is a key indicator of sympathetic nervous system (SNS) arousal.
• Single-sensor analysis is limited; multimodal systems integrating EDA with other biosignals (HRV, PPG, SKT, SpO2) are emerging.
• Existing reviews primarily focus on EDA alone, neglecting multimodal approaches.

Purpose of the Study:

• To provide a comprehensive analysis of multimodal wearable systems for SNS arousal assessment.
• To map the current landscape of sensor integration and signal processing techniques.
• To identify advancements and future research directions in context-aware SNS arousal sensing.

Main Methods:

• Systematic literature review of 58 studies meeting inclusion criteria.
• Analysis of multimodal sensor integration, from single signals to complex sensor-fusion.
• Examination of signal processing and machine learning techniques for arousal inference.

Main Results:

• Multimodal systems offer a more comprehensive assessment of physiological arousal than EDA alone.
• Advances in sensor models, physiological modeling, and context-aware sensing are evident.
• Significant progress in signal processing and machine learning enhances SNS arousal inference.

Conclusions:

• Multimodal sensing represents a significant methodological shift in physiological arousal research.
• The integration of multiple biosignals provides richer insights into SNS activity.
• This review serves as a foundational resource for developing next-generation arousal-sensing technologies.