SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection

TL;DR

SELFCARE is a wrist-based stress detection method that adaptively fuses sensor data based on context, achieving high accuracy and energy efficiency on real hardware, addressing noise robustness and power constraints.

Contribution

It introduces a novel context-aware sensor fusion approach that dynamically adjusts sensor usage, improving stress detection accuracy and energy efficiency on low-power wearable devices.

Findings

Achieves 86.34% accuracy for 3-class stress detection

Up to 2.7x energy efficiency on real hardware

Outperforms traditional sensor fusion methods

Abstract

Detecting human stress levels and emotional states with physiological body-worn sensors is a complex task, but one with many health-related benefits. Robustness to sensor measurement noise and energy efficiency of low-power devices remain key challenges in stress detection. We propose SELFCARE, a fully wrist-based method for stress detection that employs context-aware selective sensor fusion that dynamically adapts based on data from the sensors. Our method uses motion to determine the context of the system and learns to adjust the fused sensors accordingly, improving performance while maintaining energy efficiency. SELF-CARE obtains state-of-the-art performance across the publicly available WESAD dataset, achieving 86.34% and 94.12% accuracy for the 3-class and 2-class classification problems, respectively. Evaluation on real hardware shows that our approach achieves up to 2.2x (3-class) and 2.7x (2-class) energy efficiency compared to traditional sensor fusion.