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

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