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Real-Time Monitoring of Passenger’s Psychological Stress

Author

Listed:
  • Gaël Vila

    (Univ. Grenoble Alpes & CEA, LETI, MINATEC Campus, F-38054 Grenoble, France)

  • Christelle Godin

    (Univ. Grenoble Alpes & CEA, LETI, MINATEC Campus, F-38054 Grenoble, France)

  • Oumayma Sakri

    (Univ. Grenoble Alpes & CEA, LETI, MINATEC Campus, F-38054 Grenoble, France)

  • Etienne Labyt

    (Univ. Grenoble Alpes & CEA, LETI, MINATEC Campus, F-38054 Grenoble, France)

  • Audrey Vidal

    (Univ. Grenoble Alpes & CEA, LETI, MINATEC Campus, F-38054 Grenoble, France)

  • Sylvie Charbonnier

    (Gipsa-Lab, Univ. Grenoble Alpes & CNRS, F-38402 Grenoble, France)

  • Simon Ollander

    (Univ. Grenoble Alpes & CEA, LETI, MINATEC Campus, F-38054 Grenoble, France)

  • Aurélie Campagne

    (Univ. Grenoble Alpes, CNRS, LPNC, 38000 Grenoble, France)

Abstract

This article addresses the question of passengers’ experience through different transport modes. It presents the main results of a pilot study, for which stress levels experienced by a traveller were assessed and predicted over two long journeys. Accelerometer measures and several physiological signals (electrodermal activity, blood volume pulse and skin temperature) were recorded using a smart wristband while travelling from Grenoble to Bilbao. Based on user’s feedback, three events of high stress and one period of moderate activity with low stress were identified offline. Over these periods, feature extraction and machine learning were performed from the collected sensor data to build a personalized regressive model, with user’s stress levels as output. A smartphone application has been developed on its basis, in order to record and visualize a timely estimated stress level using traveler’s physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user’s stress levels were predicted in real time by the smartphone application. The number of correctly classified stress-less time windows ranged from 92.6% to 100%, depending on participant’s level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger’s stress while travelling.

Suggested Citation

  • Gaël Vila & Christelle Godin & Oumayma Sakri & Etienne Labyt & Audrey Vidal & Sylvie Charbonnier & Simon Ollander & Aurélie Campagne, 2019. "Real-Time Monitoring of Passenger’s Psychological Stress," Future Internet, MDPI, vol. 11(5), pages 1-11, April.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:5:p:102-:d:226153
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    References listed on IDEAS

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    1. Cox, Tom & Houdmont, Jonathan & Griffiths, Amanda, 2006. "Rail passenger crowding, stress, health and safety in Britain," Transportation Research Part A: Policy and Practice, Elsevier, vol. 40(3), pages 244-258, March.
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    Cited by:

    1. Loo, Becky P.Y. & Tsoi, Ka Ho, 2024. "Stressors for bus commuters and ways of improving bus journeys," Transportation Research Part A: Policy and Practice, Elsevier, vol. 187(C).

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