Stress Detection Using Wearable Devices based on Transfer Learning
文献类型：会议论文
| 作者 | Jinting Wu1,2 ; Yujia Zhang2; Xiaoguang Zhao2
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| 出版日期 | 2021-12 |
| 会议日期 | 2021-12 |
| 会议地点 | Online |
| 关键词 | Stress Detection Transfer Learning Physiological Signal Processing Wearable Devices |
| 英文摘要 | 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.
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| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/49702]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进机器人控制团队 |
| 通讯作者 | Yujia Zhang |
| 作者单位 | 1.University of Chinese Academy of Sciences 2.Institute of Automation, Chinese Academy of Sciences |
| 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]. 见:. Online. 2021-12. |
入库方式： OAI收割
来源：自动化研究所
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