论文标题
自我护理：与上下文感知的低功率边缘计算的选择性融合以进行压力检测
SELF-CARE: Selective Fusion with Context-Aware Low-Power Edge Computing for Stress Detection
论文作者
论文摘要
用生理磨损的传感器检测人的压力水平和情绪状态是一项复杂的任务，但具有许多与健康相关的好处。对传感器测量噪声和低功率设备的能源效率的鲁棒性仍然是压力检测的主要挑战。我们提出了自我护理，这是一种基于腕部的完全基于压力检测的方法，该方法采用了上下文感知的选择性传感器融合，该融合会根据传感器的数据动态适应。我们的方法使用运动来确定系统的上下文并学会相应地调整融合传感器，从而提高性能，同时保持能源效率。自我护理在公开可用的WESAD数据集中获得最先进的性能，分别针对3级和2级分类问题获得了86.34％和94.12％的精度。对真实硬件的评估表明，与传统的传感器融合相比，我们的方法可达到2.2倍（3级）和2.7倍（2级）能源效率。
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.
