Tracing Human Stress from Physiological Signals using UWB Radar

TL;DR

This paper introduces DST, a noncontact UWB radar-based deep learning method for continuous human stress detection, effectively utilizing multimodal physiological signals and outperforming existing approaches.

Contribution

The paper presents a novel noncontact stress tracing method using UWB radar and deep learning, addressing user comfort and multimodal signal fusion challenges.

Findings

DST achieves 6.31% higher accuracy than baselines.

Effective extraction of multimodal signals from RF data.

Outperforms existing stress detection methods.

Abstract

Stress tracing is an important research domain that supports many applications, such as health care and stress management; and its closest related works are derived from stress detection. However, these existing works cannot well address two important challenges facing stress detection. First, most of these studies involve asking users to wear physiological sensors to detect their stress states, which has a negative impact on the user experience. Second, these studies have failed to effectively utilize multimodal physiological signals, which results in less satisfactory detection results. This paper formally defines the stress tracing problem, which emphasizes the continuous detection of human stress states. A novel deep stress tracing method, named DST, is presented. Note that DST proposes tracing human stress based on physiological signals collected by a noncontact ultrawideband radar, which is more friendly to users when collecting their physiological signals. In DST, a signal extraction module is carefully designed at first to robustly extract multimodal physiological signals from the raw RF data of the radar, even in the presence of body movement. Afterward, a multimodal fusion module is proposed in DST to ensure that the extracted multimodal physiological signals can be effectively fused and utilized. Extensive experiments are conducted on three real-world datasets, including one self-collected dataset and two publicity datasets. Experimental results show that the proposed DST method significantly outperforms all the baselines in terms of tracing human stress states. On average, DST averagely provides a 6.31% increase in detection accuracy on all datasets, compared with the best baselines.