Advertisement

Firefighter Virtual Reality Simulation for Personalized Stress Detection

  • Soeren KlingnerEmail author
  • Zhiwei Han
  • Yuanting Liu
  • Fan Fan
  • Bashar Altakrouri
  • Bruno Michel
  • Jonas Weiss
  • Arvind Sridhar
  • Sophie Mai Chau
Conference paper
  • 158 Downloads
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12325)

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.

Keywords

Virtual Reality Machine learning Dataset Stress Interactive experience Biosignal processing Internet of Things Wearables 

References

  1. 1.
    Anderson, P.L., et al.: Virtual reality exposure therapy for social anxiety disorder: a randomized controlled trial. J. Consult. Clin. Psychol. 81(5), 751 (2013)CrossRefGoogle Scholar
  2. 2.
    Jensen, A.R., Rohwer Jr., W.D.: The stroop color-word test: a review. Acta Psychol. 25, 36–93 (1966)CrossRefGoogle Scholar
  3. 3.
    Kirschbaum, C., Pirke, K.M., Hellhammer, D.H.: The ‘trier social stress test’-a tool for investigating psychobiological stress responses in a laboratory setting. Neuropsychobiology 28(1–2), 76–81 (1993)CrossRefGoogle Scholar
  4. 4.
    Koldijk, S., Sappelli, M., Verberne, S., Neerincx, M.A., Kraaij, W.: The swell knowledge work dataset for stress and user modeling research. In: Proceedings of the 16th International Conference on Multimodal Interaction, pp. 291–298 (2014)Google Scholar
  5. 5.
    McCraty, R., Shaffer, F.: Heart rate variability: new perspectives on physiological mechanisms, assessment of self-regulatory capacity, and health risk. Global Adv. Health Med. 4(1), 46–61 (2015)CrossRefGoogle Scholar
  6. 6.
    Oskooei, A., Chau, S.M., Weiss, J., Sridhar, A., Martínez, M.R., Michel, B.: Destress: deep learning for unsupervised identification of mental stress in firefighters from heart-rate variability (hrv) data. arXiv preprint arXiv:1911.13213 (2019)
  7. 7.
    Parsons, T.D., Rizzo, A.A.: Affective outcomes of virtual reality exposure therapy for anxiety and specific phobias: a meta-analysis. J. Behav. Ther. Exp. Psychiatry 39(3), 250–261 (2008)CrossRefGoogle Scholar
  8. 8.
    Pluntke, U., Gerke, S., Sridhar, A., Weiss, J., Michel, B.: Evaluation and classification of physical and psychological stress in firefighters using heart rate variability. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2207–2212. IEEE (2019)Google Scholar
  9. 9.
    Riva, G., Waterworth, J.A., Waterworth, E.L., Mantovani, F.: From intention to action: the role of presence. New Ideas Psychol. 29(1), 24–37 (2011)CrossRefGoogle Scholar
  10. 10.
    Rothbaum, B.O., et al.: Virtual reality exposure therapy for PTSD Vietnam veterans: case study. J. Traumatic Stress Off. Publ. Int. Soc. Traumatic Stress Stud. 12(2), 263–271 (1999)CrossRefGoogle Scholar
  11. 11.
    Schmidt, P., Reiss, A., Duerichen, R., Marberger, C., Van Laerhoven, K.: Introducing WESAD, a multimodal dataset for wearable stress and affect detection. In: Proceedings of the 20th ACM International Conference on Multimodal Interaction, pp. 400–408 (2018)Google Scholar
  12. 12.
    Sierro, N.: Firefighter vital sign monitoring for predicting operational readiness. EPFL Master thesisa (2020)Google Scholar
  13. 13.
    Weil, A.: Three breathing exercises. Retrieved 15 May 2017 (2016)Google Scholar
  14. 14.
    Zimmer, P., Buttlar, B., Halbeisen, G., Walther, E., Domes, G.: Virtually stressed? a refined virtual reality adaptation of the trier social stress test (TSST) induces robust endocrine responses. Psychoneuroendocrinology 101, 186–192 (2019)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Soeren Klingner
    • 1
    Email author
  • Zhiwei Han
    • 1
  • Yuanting Liu
    • 1
  • Fan Fan
    • 1
  • Bashar Altakrouri
    • 2
  • Bruno Michel
    • 3
  • Jonas Weiss
    • 3
  • Arvind Sridhar
    • 3
  • Sophie Mai Chau
    • 3
  1. 1.fortiss GmbHMunichGermany
  2. 2.IBM Deutschland GmbHMunichGermany
  3. 3.IBM Zurich Research LabRüschlikonSwitzerland

Personalised recommendations