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

arXiv: https://arxiv.org/abs/2205.03974 · HTML: https://mlsanity.com/paper/2205.03974/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 20 references that are in the map.
- First posted on arXiv 8 May 2022.

## Builds on (most cited first, 6 of 6)

- [HydraFusion: Context-Aware Selective Sensor Fusion for Robust and Efficient Autonomous Vehicle Perception](https://mlsanity.com/paper/2201.06644/) (2022)
- [EcoFusion: Energy-Aware Adaptive Sensor Fusion for Efficient Autonomous Vehicle Perception](https://mlsanity.com/paper/2202.11330/) (2022)
- [AHAR: Adaptive CNN for Energy-efficient Human Activity Recognition in Low-power Edge Devices](https://mlsanity.com/paper/2102.01875/) (2021)
- [StressNAS: Affect State and Stress Detection Using Neural Architecture Search](https://mlsanity.com/paper/2108.12502/) (2021)
- [Feature Augmented Hybrid CNN for Stress Recognition Using Wrist-based Photoplethysmography Sensor](https://mlsanity.com/paper/2108.03166/) (2021)
- [Kalman Filter-based Heuristic Ensemble (KFHE): A new perspective on multi-class ensemble classification using Kalman filters](https://mlsanity.com/paper/1807.11429/) (2018)

## Built on by (most cited first, 2 of 2)

- [A Survey on Dynamic Neural Networks: from Computer Vision to Multi-modal Sensor Fusion](https://mlsanity.com/paper/2501.07451/) (2025)
- [Artifact Annotations Partially Substitute for Per-User Calibration: SAFE-EDA and a Normalization-Controlled Evaluation of Wrist-EDA Affect Recognition](https://mlsanity.com/paper/2610.01692/) (2026)

## Abstract (arXiv)

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.