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

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 clas...

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Bibliographic Details
Published inInternational journal of machine learning and cybernetics Vol. 11; no. 8; pp. 1825 - 1837
Main Authors Shan, Yuhao, Li, Shigang, Chen, Tong
Format Journal Article
LanguageEnglish
Published Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2020
Springer Nature B.V
Subjects
Affect (Psychology)
Artificial Intelligence
Classification
Complex Systems
Computational Intelligence
Contact stresses
Control
Digital cameras
Emotion recognition
Engineering
Feature selection
Heart rate
Heat detection
Homeostasis
Mechatronics
Original Article
Pattern Recognition
Physical stress
Physiology
Psychological stress
Remote sensors
Respiration
Robotics
Sensors
Stress state
Systems Biology
Stress detection
Physiological features of stress
Stress classification
Remote sensing respiration signal
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Summary: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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ISSN:1868-8071
1868-808X
DOI:10.1007/s13042-020-01074-x