Semantic Decision Support for Action Forces with Risk Stratification from Estimated Physiological Strain, Cognitive-Emotional Stress and Situation Awareness

Abstract

In the life-threatening work of action forces, a decision support system (DSS) must provide a software application that should improve a mission decision maker's capability to make decisions. This requires analysing large amounts of data and to present and visualize the best possible options available. In case of first responders, where errors in decision-making can have fatal consequences, timely identification of increased risk of physiological collapse, insufficient cognitive readiness and lack of situation awareness is mandatory. This paper therefore introduces our Semantic Decision Support System (SDSS) that can apply intelligent analytics on data from wearable biosignal sensors, to provide feedback in terms of risk stratification. It also includes a recommender engine that identifies the best next action at team management level. Its novelty lies specifically in the combination of various multimodal data streams each being equipped with assessment modules, risk stratification and recommender engines in order to finally combine various aspects of decision support that is based on psychophysiological measurement technologies. All relevant data is systematically merged into an advanced expert dashboard, providing a comprehensive platform for the continuous real-time monitoring and visualization of critical information. This capability enables the ongoing assessment of risk levels associated with a diverse group of action forces. The centralized dashboard serves as a powerful tool, enabling careful surveillance and prompt response to emerging risks across a broad spectrum of operational scenarios.

Keywords: Estimated Physiological Strain, Cognitive-Emotional Stress, Situation Awareness, Semantic Decision Support, Expert Dashboard

DOI: 10.54941/ahfe1004700