Open AccessCarrying a passenger and relaxation before driving: Classification of young drivers’ physiological activation
Author(s) -
Meteier Quentin,
Capallera Marine,
De Salis Emmanuel,
Widmer Marino,
Angelini Leonardo,
Abou Khaled Omar,
Mugellini Elena,
Sonderegger Andreas
Publication year - 2022
Publication title -
physiological reports
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.918
H-Index - 39
ISSN - 2051-817X
DOI - 10.14814/phy2.15229
Subject(s) - relaxation (psychology) , driving simulator , stressor , human factors and ergonomics , poison control , computer science , session (web analytics) , active listening , injury prevention , simulation , psychology , applied psychology , audiology , medicine , social psychology , clinical psychology , communication , medical emergency , world wide web
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.