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24-02-2020 | Original Article | Issue 8/2020

International Journal of Machine Learning and Cybernetics 8/2020

Respiratory signal and human stress: non-contact detection of stress with a low-cost depth sensing camera

Journal:
International Journal of Machine Learning and Cybernetics > Issue 8/2020
Authors:
Yuhao Shan, Shigang Li, Tong Chen
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The online version of this article (https://​doi.​org/​10.​1007/​s13042-020-01074-x) contains supplementary material, which is available to authorized users.

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Abstract

Psychological stress may cause various health problems. To prevent the potential chronic illness that long-term psychological stress could cause, it is important to detect and monitor the psychological stress at its initial stage. In this paper, we present a framework for remotely detecting and classifying human stress by using a KINECT sensor that is portable and affordable enough for ordinary users in everyday life. Unlike most of emotion recognition tasks in which respiratory signals (RSPS) are usually used only as an aiding analysis, the whole task proposed is based on RSPS. Thus, the main contribution of this paper is that not only the non-contact devices is used to identify human stress, but also the relationship between RSPS and stress recognition is analyzed in detail. Experimental results on 84 volunteers show that the recognition accuracy for recognizing psychological stress, physical stress, and relaxing state are 93.90%, 93.40%, and 89.05% respectively. These results suggest that the proposed framework is effective for monitoring human stress, and RSPS could be used for stress recognition.

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