Contactless Nighttime Stress Monitoring with mmWave Radar

Abstract

Contactless stress monitoring, with its non-intrusive nature, is invaluable for maintaining mental and physical health. Recent studies have demonstrated encouraging results in contactless stress monitoring during daytime using radio frequency (RF) signals. However, the weak correlation between stress levels and behaviors during the night poses a significant challenge in stress monitoring, which remains unsolved. In this paper, we propose mmWave Nighttime Stress monitoring (mmNS), a learning-based end-to-end framework for contactless nighttime stress monitoring. Specifically, this framework incorporates radar signal processing and a self-supervised physiological feature separation strategy, combined with a signal complexity-oriented network design, to effectively extract and encode periodic physiological features for accurate stress level classification. To evaluate the stress monitoring performance of mmNS, we collect a RF-based nighttime stress monitoring dataset, which contains stress data from 10 volunteers. The experimental results demonstrate that our method achieves state-of-the-art stress monitoring performance, about 76% accuracy and 72% F1-score in classifying low, medium and high stress. To our knowledge, this is the first attempt dealing with contactless nighttime stress monitoring.