Evaluation of driver stress level with survey, galvanic skin response sensor data, and force-sensing resistor data. (December 2019)

• Record Type: Journal Article
• Authors: Dogan, Daghan; Bogosyan, Seta; Acarman, Tankut
• Is Part Of: Advances in mechanical engineering. Volume 11:Number 12(2019)
• Publication Date: 2019-12
• Subjects: Driver -- force-sensing resistor -- galvanic skin response sensor -- stress level -- statistical analysis -- condition monitoring; Mechanical engineering -- Periodicals; 621.05
• DOI: 10.1177/1687814019891555
• Languages: English
• ISSNs: 1687-8132

Abstract

Thousands of lives are lost in traffic accidents every year, and most traffic accidents are caused by driver errors. Causes and impairments such as fatigue, inattentiveness, alcohol usage, stress, and drugs are the main factors of these accidents. When a driver is subject to changing and complicated driving tasks in traffic, he or she should be able to assure driving authority to prevent potential hazards and accidents. In this context, the purpose of this study is to determine the stress level of the driver when driving in urban traffic in such situations requiring delegation of driving authority. Thus, the work combines stress questionnaire and galvanic skin response sensor to validate results and fuses with a force-sensing resistor. In this study, a prototype electric vehicle is equipped with sensors providing various drivers' data including the responses of a force-sensing resistor sensor while galvanic skin is being collected on a specified route. At the end of the trip, the stress level of the drivers is determined by the collected data. Results indicate that the galvanic skin sensor stress results are consistent with the results of the survey with an average accuracy of 87.5%. The force-sensing resistor sensor is only used to determine gender stress. And the force-sensing resistor sensor gender-stress results are consistent with results of the survey with an accuracy of 100%. These results are used to validate the results of post-driving stress survey evaluated by SPSS 23.0 windows statistics software. Data analysis is particularly focused on demographic properties of participators, factor analysis, reliability tests, correlation, T-test, and one-way analysis of variance.