Abstract
Classifying stress in firefighters poses challenges, such as accurate personalized labeling, unobtrusive recording, and training of adequate models. Acquisition of labeled data and verification in cage mazes or during hot trainings is time consuming. Virtual Reality (VR) and Internet of Things (IoT) wearables provide new opportunities to create better stressors for firefighter missions through an immersive simulation. In this demo, we present a VR-based setup that enables to simulate firefighter missions to trigger and more easily record specific stress levels. The goal is to create labeled datasets for personalized multilevel stress detection models that include multiple biosignals, such as heart rate variability from electrocardiographic RR intervals. The multi-level stress setups can be configured, consisting of different levels of mental stressors. The demo shows how we established the recording of a baseline and virtual missions with varying challenge levels to create a personalized stress calibration.
This research was co-funded by the Bavarian Ministry of Economic Affairs, Regional Development and Energy, project Dependable AI, IBM Deutschland GmbH, and IBM Research, and was carried out within the Center for AI jointly founded by IBM and fortiss.
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Klingner, S. et al. (2020). Firefighter Virtual Reality Simulation for Personalized Stress Detection. In: Schmid, U., Klügl, F., Wolter, D. (eds) KI 2020: Advances in Artificial Intelligence. KI 2020. Lecture Notes in Computer Science(), vol 12325. Springer, Cham. https://doi.org/10.1007/978-3-030-58285-2_32
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