Measure and assess the stress level of a firefighter in real time
A large number of firefighters are injured every year due to improper reactions in dangerous operations. Extreme heat, poor visibility due to smoke development, time pressure, are only some of the external factors under which they must react quickly. The stress that arises in such a situation affects physical and mental reactions and can lead to a potentially severe impairment of cognitive abilities.
However, the person affected is often unaware of his or her limited ability to judge. It is therefore crucial to provide firefighters with more accurate information about the potential danger in relation to their current physical and emotional state. fortiss is working on a way to measure and assess the stress level of a firefighter in real time in order to support the firefighter's decisions directly on the job.
In this project fortiss investigates mental stress (both physical and mental) and develops user-centered machine learning algorithms for stress monitoring. They serve to improve the system-internal representation of the human and based on data mining as well as the acquisition of cognitive characteristics. Among other things, general indicators of stress such as heart rate, brain activity, muscle tension, skin moisture or cortisone secretion are investigated. Individual reactions to stress are also taken into account, which depends not only on the individual's current mental capacity, but also on the situation he or she is in during the measurement. The resulting data-based system is designed to better meet the requirements and needs of firefighters.
fortiss is developing new personalized stress detection models based on different fire brigade scenarios and the experiences from such missions, in order to provide a comprehensible user stress state. The aim of the project is to increase the performance of an intelligent learning system.
16.12.2019 - 31.12.2023