Tracing Human Stress From Physiological Signals Using UWB Radar

Jia Xu, Teng Xiao, Pin Lv, Zhe Chen, Chao Cai, Yang Zhang, Zehui Xiong

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 the 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 the multimodal physiological signals, which results in less satisfactory detection results. This article formally defines the stress tracing problem, which emphasizes the continuous detection of human stress states. A novel deep stress tracing (DST) method, named DST, is presented. Note that, DST proposes tracing human stress based on the 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 the 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 the three real-world data sets, including one self-collected data set and two publicity data sets. 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 the data sets, compared with the best baselines.

External IDs:dblp:journals/iotj/XuXLCCZX24