Wearable Systems and Methods for Monitoring Psychological and Physical Condition of Soldiers

Abstract

From the second half of the 1990s, thanks to more affordable and more powerful information technology and electronical systems for recording based on miniaturized sensors, we can observe a more intensive development of a method of data analysis and a system that monitors the physical and psychological conditions of soldiers. Systems for measuring and evaluation methods of physical and medical data for the diagnostics of physical and psychological state have significantly spread, especially in clinical practice. This study, however, examines the current technologies and usage of the wearable monitoring systems in military field. The result of this study is not only the not-yet published summary of the current state, but also the proposed possibilities of usage and recommendation of specific systems, their sensors and system methods in the military. Thus, the article can serve as a guide for choosing suitable and affordable electronical wearable systems and methods of quantitative evaluation of physical and psychological conditions of people in the military.

An extension of the shifted Wald model of human response times: Capturing the time dynamic properties of human cognition

“…Quantifying even the amount of variability in parameters has proven very difficult (Boehm et al, 2018;Ratcliff & Tuerlinckx, 2002) and only a handful of studies to date have attempted to quantify trial-to-trial variation in EAM parameters (Boehm et al, 2014;Gunawan et al, 2022;van Maanen et al, 2011) with some limiting factors (e.g., the full data set was required in advance). To-date, most applications of operator-state triggered adaptive automation have explored the use of wearable psycho-physiological monitoring technologies (e.g., ECG, EEG), but there is limited evidence for the diagnosticity and predictive power of physiological measures for workload estimation (Charles & Nixon, 2019), and thus it remains unclear the extent to which metrics hold practical or operational advantages in real world contexts (Kutilek et al, 2017;Wilson & Eggemeier, 2020). We argue that EAMs that can quantify the latent cognitive workload of a human operator would be a more natural solution in this space if appropriate models could be developed.…”