Real-Time Monitoring of Passenger’s Psychological Stress

Gaël Vila, Christelle Godin, Oumayma Sakri, Etienne Labyt, Audrey Vidal, Sylvie Charbonnier, Simon Ollander and Aurélie Campagne
Additional contact information
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

Future Internet, 2019, vol. 11, issue 5, 1-11

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.

Keywords: wellbeing; stress; physiology; wearable sensors; human-machine interface; affective computing (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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