{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,16]],"date-time":"2026-02-16T20:48:08Z","timestamp":1771274888240,"version":"3.50.1"},"reference-count":14,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T00:00:00Z","timestamp":1556236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010680","name":"H2020 Transport","doi-asserted-by":"publisher","award":["635867"],"award-info":[{"award-number":["635867"]}],"id":[{"id":"10.13039\/100010680","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Future Internet"],"abstract":"<jats:p>This article addresses the question of passengers\u2019 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\u2019s 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\u2019s 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\u2019s physiological signals. This setting was put on test during another travel from Grenoble to Brussels, where the same user\u2019s 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\u2019s level of activity. By design, this study represents a first step for real-life, ambulatory monitoring of passenger\u2019s stress while travelling.<\/jats:p>","DOI":"10.3390\/fi11050102","type":"journal-article","created":{"date-parts":[[2019,4,26]],"date-time":"2019-04-26T08:26:33Z","timestamp":1556267193000},"page":"102","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Real-Time Monitoring of Passenger\u2019s Psychological Stress"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9778-8206","authenticated-orcid":false,"given":"Ga\u00ebl","family":"Vila","sequence":"first","affiliation":[{"name":"Univ. Grenoble Alpes &amp; CEA, LETI, MINATEC Campus, F-38054 Grenoble, France"}]},{"given":"Christelle","family":"Godin","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes &amp; CEA, LETI, MINATEC Campus, F-38054 Grenoble, France"}]},{"given":"Oumayma","family":"Sakri","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes &amp; CEA, LETI, MINATEC Campus, F-38054 Grenoble, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8680-7213","authenticated-orcid":false,"given":"Etienne","family":"Labyt","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes &amp; CEA, LETI, MINATEC Campus, F-38054 Grenoble, France"}]},{"given":"Audrey","family":"Vidal","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes &amp; CEA, LETI, MINATEC Campus, F-38054 Grenoble, France"}]},{"given":"Sylvie","family":"Charbonnier","sequence":"additional","affiliation":[{"name":"Gipsa-Lab, Univ. Grenoble Alpes &amp; CNRS, F-38402 Grenoble, France"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8227-052X","authenticated-orcid":false,"given":"Simon","family":"Ollander","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes &amp; CEA, LETI, MINATEC Campus, F-38054 Grenoble, France"}]},{"given":"Aur\u00e9lie","family":"Campagne","sequence":"additional","affiliation":[{"name":"Univ. Grenoble Alpes, CNRS, LPNC, 38000 Grenoble, France"}]}],"member":"1968","published-online":{"date-parts":[[2019,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/S0006-8993(00)02950-4","article-title":"The neurobiology of stress: from serendipity to clinical relevance","volume":"886","author":"McEwen","year":"2000","journal-title":"Brain Res."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1002\/per.2410010304","article-title":"Transactional theory and research on emotions and coping","volume":"1","author":"Lazarus","year":"1987","journal-title":"Eur. J. Personal."},{"key":"ref_3","unstructured":"European Foundation for the Improvement of Living and Working Conditions (1984). 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Part Policy Pract."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1016\/j.trf.2010.11.008","article-title":"Comparing stress of car and train commuters","volume":"14","author":"Wener","year":"2011","journal-title":"Transp. Res. Part F Traffic Psychol. Behav."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/MCE.2016.2590178","article-title":"A Survey of Affective Computing for Stress Detection: Evaluating technologies in stress detection for better health","volume":"5","author":"Greene","year":"2016","journal-title":"IEEE Consum. Electron. Mag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"235","DOI":"10.30773\/pi.2017.08.17","article-title":"Stress and Heart Rate Variability: A Meta-Analysis and Review of the Literature","volume":"15","author":"Kim","year":"2018","journal-title":"Psychiatry Invest."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Boucsein, W. (2012). Electrodermal Activity, Springer Science & Business Media.","DOI":"10.1007\/978-1-4614-1126-0"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1109\/TITS.2005.848368","article-title":"Detecting stress during real-world driving tasks using physiological sensors","volume":"6","author":"Healey","year":"2005","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_11","first-page":"493","article-title":"Stress: Towards a Gold Standard for Continuous Stress Assessment in the Mobile Environment","volume":"2015","author":"Hovsepian","year":"2015","journal-title":"Proc. ACM Int. Conf. Ubiquitous Comput. UbiComp Conf."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Ollander, S., Godin, C., Campagne, A., and Charbonnier, S. (2016, January 9\u201312). A comparison of wearable and stationary sensors for stress detection. 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Available online: https:\/\/www.mathworks.com\/help\/signal\/ug\/prominence.html."}],"container-title":["Future Internet"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/5\/102\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:47:20Z","timestamp":1760186840000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1999-5903\/11\/5\/102"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,26]]},"references-count":14,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2019,5]]}},"alternative-id":["fi11050102"],"URL":"https:\/\/doi.org\/10.3390\/fi11050102","relation":{},"ISSN":["1999-5903"],"issn-type":[{"value":"1999-5903","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,4,26]]}}}