Intelligent Stress Monitoring Assistant for First Responders

This paper describes a prototype of an intelligent Stress Monitoring Assistant (SMA), - the next generation of stress detectors. The SMA is intended for the first responders and professionals coping with exposure to extreme physical and psychological stressors, e.g. firefighters, combat military per...

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Bibliographic Details
Published in:IEEE access 2021, Vol.9, p.25314-25329
Main Authors: Lai, Kenneth, Yanushkevich, Svetlana N., Shmerko, Vlad P.
Format: Article
Language:English
Subjects:
affect recognition
Biomedical monitoring
causal inference
Cognition
Convolution
Datasets
Decision making
Decision support systems
Detectors
Emergency medical services
Emergency response
Firefighters
intelligent decision support
machine reasoning
Military personnel
Monitoring
Ordnance
Physiological effects
Prototypes
Sensors
Soft biometrics
Stress
stress monitoring
Stress propagation
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Summary:This paper describes a prototype of an intelligent Stress Monitoring Assistant (SMA), - the next generation of stress detectors. The SMA is intended for the first responders and professionals coping with exposure to extreme physical and psychological stressors, e.g. firefighters, combat military personnel, explosive ordnance disposal operatives, law enforcement officers, emergency medical technicians, and paramedics. Stress impacts human behavior and decision-making, which can be propagated between the team members. The SMA is an integral part of the Decision Support System, it is a component of the decision support perception-action cycle. We model this cycle as a cognitive dynamic system. The intelligent part of the SMA is designed using a ) a residual-temporal convolution network for learning data from sensors and detection of stress features, and b ) a reasoning mechanism based on a causal network for fusion at various levels. The SMA prototype has been tested using a multi-factor physiological dataset WEarable Stress and Affect Detection (WESAD). In both modes, the stress recognition and stress detection , the SMA achieves an accuracy of 86% and 98% for the WESAD dataset, respectively. This performance is superior to the known results in satisfying the requirements of reliable decision support.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2021.3057578