Stress Detection Using Wearable Devices based on Transfer Learning

	Stress Detection Using Wearable Devices based on Transfer Learning
	Jinting Wu1,2 ; Yujia Zhang1 ; Xiaoguang Zhao1
	2021-12
会议名称	2021 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
会议日期	2021-12
会议地点	Online
摘要	Excessive stress will have a negative impact on people's physical and mental health, especially for some special occupations. Because stressful stimuli can trigger a variety of physiological responses, analyzing physiological signals collected by wearable devices has become an important way to evaluate the stress state in recent years. However, the number of available subjects of a target group may be small, and collecting a large amount of data when the target group changes is costly and time-consuming. To solve this problem, we propose a stress detection framework for a small target group which uses adversarial transfer learning method to learn shared knowledge about stress between different groups. In order to verify the performance of the framework, we establish a dataset consisting of 264 ordinary college students and 32 police school students, aiming to evaluate the acute stress state of police school students under video stimuli for psychological training in the future. Comprehensive experiments show that our algorithm has achieved a signifificant improvement in the target group compared with the baseline methods.
关键词	Stress Detection Transfer Learning Physiological Signal Processing Wearable Devices
收录类别	EI
语种	英语
七大方向——子方向分类	人工智能+医疗
国重实验室规划方向分类	其他
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49702
专题	复杂系统认知与决策实验室_先进机器人
通讯作者	Yujia Zhang
作者单位	1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Jinting Wu,Yujia Zhang,Xiaoguang Zhao. Stress Detection Using Wearable Devices based on Transfer Learning[C],2021.

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