Key result
The intelligent Stress Monitoring Assistant achieved an accuracy of 86% for stress recognition and 98% for stress detection using physiological data from the WESAD dataset.
Why the study?
Stress impacts human behavior and decision-making among first responders and professionals exposed to extreme physical and psychological stressors, which can propagate between team members.
The proposed Intelligent Stress Monitoring Assistant prototype achieved high accuracy (up to 98%) in detecting stress using physiological data from the WESAD dataset, offering a potential decision support tool for first responders.
Offers a promising prototype for physiological stress monitoring in first responders; leaves open real-world clinical utility.
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.
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Lai et al. (2021) studied Stress (n=17). Intelligent Stress Monitoring Assistant (SMA) was evaluated on Stress detection accuracy. The intelligent Stress Monitoring Assistant achieved an accuracy of 86% for stress recognition and 98% for stress detection using physiological data from the WESAD dataset.
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