Abstract
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.
Highlights
Humans have three primary systems vital for survival: vision, cognitive processing, and motor skill (V-C-M)
This model provides the necessary conditions for the incorporation of computational intelligence in the form of Machine Learning (ML) and machine reasoning
Our work partially covers sufficient conditions as follows: 1) The SMA design uses the advances in machine learning, in particular, the Residual-Temporal Convolution Network (Res-TCN). 2) The SMA intelligence is realized by probabilistic reasoning; it performs a fusion of the stress-related features
Summary
Humans have three primary systems vital for survival: vision, cognitive processing, and motor skill (V-C-M). STRESS MONITORING MODEL Design requirements to stress monitoring can be met using the principles of a cognitive dynamic system This system is based on Haykin’s model [17]: Perception-action cycle implies that there are perceptor and actuator; Memory for the purpose to learn from the environment and store knowledge; Attention – ability to prioritize the allocation of available resources; and Intelligence– a function that enables the control and decisionmaking mechanism to help identify intelligent choices. The SMA is a component of decision support built on the principle of stress perception-action cycle known as a General Adaptation Syndrome (GAS) [18] This model provides the necessary conditions for the incorporation of computational intelligence in the form of Machine Learning (ML) and machine reasoning. For example, real-life scenario data becomes available, the learning and reasoning mechanism will make it possible to adjust to the new data
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