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Driver Stress Detection in Simulated Driving Scenarios with Photoplethysmography

  • Conference paper
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Distributed Computing and Artificial Intelligence, 19th International Conference (DCAI 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 583))

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Abstract

The great advances in technology have allowed the development of portable devices capable of monitoring different physiological measures in an inexpensive, non-invasive and efficient way. Virtual Reality (VR) has also evolved achieving a very realistic immersive experience in different contexts. The combination of signal acquisition devices and VR makes it possible to generate useful knowledge even in challenging situations of daily life, such as when driving. The treatment through artificial intelligence techniques allows the development of systems for the recognition of vital emotions to control human health and safety. The present work investigates the feasibility of detecting stress in individuals using physiological signals collected with a photoplethysmography (PPG) sensor incorporated into a commonly used wristwatch. The characteristics acquired during an immersive model through VR simulation are taken as input to a trained model by Machine Learning (ML) algorithms. This model performs the driver stress detection and the high precision classification in real time. By means of several immersive experiments, the validation of the proposed system is checked through the Heart Rate Average (HRV) for the identification of driver stress.

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Notes

  1. 1.

    Leap Motion: https://www.ultraleap.com/product/leap-motion-controller/.

  2. 2.

    Unreal Engine 4: https://www.unrealengine.com/en-US/.

  3. 3.

    HTC Vive: https://www.vive.com/mx/product/vive-pro/.

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Acknowledgments

This research has been supported by the project RTI2018-095390-B-C32 (MCIU/AEI/FEDER, UE).

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Authors and Affiliations

  1. BISITE Research Group, University of Salamanca, Edificio Multiusos I+D+i, 37007, Salamanca, Spain

    Nuria Mateos-García, Ana B. Gil-González, Ana de Luis Reboredo & Belén Pérez-Lancho

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  1. Nuria Mateos-García
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  2. Ana B. Gil-González
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  3. Ana de Luis Reboredo
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  4. Belén Pérez-Lancho
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Corresponding author

Correspondence to Ana B. Gil-González .

Editor information

Editors and Affiliations

  1. Hiroshima University, Hiroshima, Japan

    Sigeru Omatu

  2. King Abdulaziz University, Jeddah, Saudi Arabia

    Rashid Mehmood

  3. Kielce University of Technology, Kielce, Poland

    Pawel Sitek

  4. Palazzo Camponeschi, University of L'Aquila, L'Aquila, Italy

    Serafino Cicerone

  5. BISITE, Edificio I+D+i, University of Salamanca, Salamanca, Spain

    Sara Rodríguez

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Mateos-García, N., Gil-González, A.B., Reboredo, A.d.L., Pérez-Lancho, B. (2023). Driver Stress Detection in Simulated Driving Scenarios with Photoplethysmography. In: Omatu, S., Mehmood, R., Sitek, P., Cicerone, S., Rodríguez, S. (eds) Distributed Computing and Artificial Intelligence, 19th International Conference. DCAI 2022. Lecture Notes in Networks and Systems, vol 583. Springer, Cham. https://doi.org/10.1007/978-3-031-20859-1_29

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