Carrying a passenger and relaxation before driving: Classification of young drivers’ physiological activation

Meteier, Quentin; Capallera, Marine; De Salis, Emmanuel; Widmer, Marino; Angelini, Leonardo; Abou Khaled, Omar; Mugellini, Elena; Sonderegger, Andreas (2022). Carrying a passenger and relaxation before driving: Classification of young drivers’ physiological activation Physiological Reports, 10(10), pp. 1-17. John Wiley & Sons 10.14814/phy2.15229

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Drivers are often held responsible for road crashes. Previous research has shown that stressors such as carrying passengers in the vehicle can be a source of accidents for young drivers. To mitigate this problem, this study investigated whether the presence of a passenger behind the wheel can be predicted using machine learning, based on physiological signals. It also addresses the question whether relaxation before driving can positively influence the driver's state and help controlling the potential negative consequences of stressors. Sixty young participants completed a 10-min driving simulator session, either alone or with a passenger. Before their driving session, participants spent 10 min relaxing or listening to an audiobook. Physiological signals were recorded throughout the experiment. Results show that drivers experience a higher increase in skin conductance when driving with a passenger, which can be predicted with 90%-accuracy by a k-nearest neighbors classifier. This might be a possible explanation for increased risk taking in this age group. Besides, the practice of relaxation can be predicted with 80% accuracy using a neural network. According to the statistical analysis, the potential beneficial effect of relaxation did not carry out on the driver's physiological state while driving, although machine learning techniques revealed that participants who exercised relaxation before driving could be recognized with 70% accuracy. Analysis of physiological characteristics after classification revealed several relevant physiological indicators associated with the presence of a passenger and relaxation.

Item Type:	Journal Article (Original Article)
Division/Institute:	Business School > Institute for New Work Business School > Institute for New Work > Achtsamkeit und Positive Leadership Business School
Name:	Meteier, Quentin; Capallera, Marine; De Salis, Emmanuel; Widmer, Marino; Angelini, Leonardo; Abou Khaled, Omar; Mugellini, Elena and Sonderegger, Andreas0000-0003-0054-0544
Subjects:	B Philosophy. Psychology. Religion > BF Psychology Q Science > QA Mathematics > QA75 Electronic computers. Computer science
ISSN:	2051-817X
Publisher:	John Wiley & Sons
Funders:	[UNSPECIFIED] Hasler Stiftung
Language:	English
Submitter:	Andreas Sonderegger
Date Deposited:	23 May 2022 16:33
Last Modified:	23 May 2022 16:33
Publisher DOI:	10.14814/phy2.15229
Uncontrolled Keywords:	driver state, machine learning, passenger, physiology, relaxation, stress
ARBOR DOI:	10.24451/arbor.16978
URI:	https://arbor.bfh.ch/id/eprint/16978