Chronic stress is the main cause of health problems in high-risk jobs. Wearable sensors can become an ecologically valid method of stress level assessment in real-life applications. We sought to determine a non-invasive technique for objective stress monitoring. Data were collected from firefighters during 24-h shifts using sensor belts equipped with a dry-lead electrocardiograph (ECG) and a three-axial accelerometer. Levels of stress experienced during fire incidents were evaluated via a brief self-assessment questionnaire. Types of physical activity were distinguished basing on accelerometer readings, and heart rate variability (HRV) time series were segmented accordingly into corresponding fragments. Those segments were classified as stress/no-stress conditions. Receiver Operating Characteristic (ROC) analysis showed true positive classification as stress condition for 15% of incidents (while maintaining almost zero False Positive Rate), which parallels the amount of truly stressful incidents reported in the questionnaires. These results show a firm correspondence between the perceived stress level and physiological data. Psychophysiological measurements are reliable indicators of stress even in ecological settings and appear promising for chronic stress monitoring in high-risk jobs, such as firefighting.
Abstract-In this paper, we summarize AAIA'15 data mining competition: Tagging Firefighter Activities at a Fire Scene, which was held between March 9 and July 6, 2015. We describe the scope and background of the competition. We also reveal details regarding the data set used in the competition, which was collected and tagged specifically for the purpose of this data challenge. We explain the data acquisition process which involved using a body sensor network system consisting of several inertial measurement units and a physiological data sensor. Finally, we briefly discuss submitted results with respect to their possible real-life application in our decision support system.
We introduce an open-source software Aamks for fire risk assessment. This article focuses on a component of Aamks -an evacuation simulator named a-evac. A-evac models evacuation of humans in the fire environment produced by CFAST fire simulator. In the article we discuss the probabilistic evacuation approach, automatic planning of exit routes, the interactions amongst the moving evacuees and the impact of smoke on the humans. The results consist of risk values based on FED, F-N curves and evacuation animations.
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