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With the progressive advancement in Information and Computer technology, the battlefield scenario is also fetching with more complexity day by day. Therefore, soldiers’ overall stress management system has to turn out to be an inescapable part of combat efficiency. During any military operation, a real-time analysis of varied stress factors that troops usually encounter will certainly facilitate the commanders at all levels in their decision-making process. Therefore, the objective of this paper is to develop an early prototype of a stress monitoring system named as Soldiers Stress Monitoring System, in abbreviated form SMS. The proposed system incorporates the brain waves and physiological data of (deployed) soldiers to quantity their stress level and assists the higher commander to take the best decision about troops’ deployment (continue/return). The Electroencephalography (EEG) based Brain-Computer Interfacing (BCI) technology was used to acquire the brain signals, while machine learning was adopted to facilitate the decision-making process for the higher commander. A lightweight evaluation study was also carried out where it has been found that the identified stress factors and the decision assistance procedures of the proposed system were effective and efficient for deciding on the military operation by the higher command. Moreover, the study showed that the Bayes Net algorithm demonstrated a better accuracy of 98.4% followed by the JRip algorithm of 97.5% while categorizing the soldiers’ stress status.
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