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.

We introduce an open-source software for fire risk assessment named Aamks. 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 a zone fire model 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 humans. The results consist of risk values based on fractional effective dose and are presented in the form of various probability distributions and evacuation animations. The intended scope of Aamks is buildings, e.g., offices, malls, factories rather than stadiums or streets. We present the need for such software based on the current state of research and existing engineering tools in the probabilistic risk assessment domain. Then we describe a-evac and its details: geometry, path-finding, local movement, interaction with fire, and visualization. Given the above scope, the article contributes to the domain of probabilistic risk assessment by proposing: (a) stochastic approach to evacuation, (b) velocity-based model for evacuation, (c) evacuation software that interacts with fire conditions and zone fire models.

We discuss how to support commanders at the fire ground. We present a risk management framework for modelling the fire phenomena and for communicating with firefighters. We claim that appropriate derivation, selection and representation of information is the crucial aspect of decision support, as it may improve the commander's perception.

The Froude-number based reduced-scale modeling is a technique commonly used to investigate the flow of heat and mass in building fires. The root of the method is the thermodynamic model of a flow in a compartment and several non-dimensional flow numbers based on the proportionalities of the Navier-Stokes and heat transfer equations. The ratio of inertial forces to the buoyancy forces, known as the Froude-number, plays a pivotal role within these proportionalities. This paper is an attempt to define the range of credible scale modeling using the Froude-number. We verify the credibility of the modeling by small fire (approximately 150 kW) in a small compartment, comparing data from a physical test (scale 1:1 and 1:4) and the numerical model’s data (Fire Dynamics Simulator, scales 1:1, 1:2, 1:4, 1:10, 1:20, and 1:50). The scope of the research covers a wide range of fires, with observed change of the flow from turbulent to laminar. The results show that the applicability of Froude-number reduced-scale modeling has limitations related to the scale. Therefore, it should be applied with care following sensibility analysis. We propose a method for sensibility analysis using Computational Fluid Dynamics (CFD) modeling.