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Real-Time Monitoring of Passenger’s Psychological Stress

1
Univ. Grenoble Alpes & CEA, LETI, MINATEC Campus, F-38054 Grenoble, France
2
Gipsa-Lab, Univ. Grenoble Alpes & CNRS, F-38402 Grenoble, France
3
Univ. Grenoble Alpes, CNRS, LPNC, 38000 Grenoble, France
*
Author to whom correspondence should be addressed.
Future Internet 2019, 11(5), 102; https://doi.org/10.3390/fi11050102
Received: 29 March 2019 / Revised: 20 April 2019 / Accepted: 24 April 2019 / Published: 26 April 2019
(This article belongs to the Special Issue Internet of Things for Smart City Applications)
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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. View Full-Text
Keywords: wellbeing; stress; physiology; wearable sensors; human-machine interface; affective computing wellbeing; stress; physiology; wearable sensors; human-machine interface; affective computing
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Vila, G.; Godin, C.; Sakri, O.; Labyt, E.; Vidal, A.; Charbonnier, S.; Ollander, S.; Campagne, A. Real-Time Monitoring of Passenger’s Psychological Stress. Future Internet 2019, 11, 102.

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