Abstract
Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%.
Highlights
According to the latest estimates by the World Health Organization, approximately 1.35 million people die each year from road traffic accidents and between 20–50 million people suffer from non-fatal injuries [1]
Conventional Advanced driver-assistance systems (ADAS) technologies are mainly based on controlling the vehicle state through proprioceptive (i.e., Odometry, inertial sensors) and exteroceptive sensors (i.e., Lidar, vision sensors, radar, infrared, and ultrasonic sensors) [2]
A novel method for driver stress evaluation based on thermal IR imaging and supervised machine learning approaches was described
Summary
According to the latest estimates by the World Health Organization, approximately 1.35 million people die each year from road traffic accidents and between 20–50 million people suffer from non-fatal injuries [1].Advanced driver-assistance systems (ADAS) are designed to support humans during the driving process, leading to an increase in road safety. Conventional ADAS technologies are mainly based on controlling the vehicle state through proprioceptive (i.e., Odometry, inertial sensors) and exteroceptive sensors (i.e., Lidar, vision sensors, radar, infrared, and ultrasonic sensors) [2]. These state-of-the-art technologies allow for the recognition of objects [3], alerting the driver about dangerous road. Sci. 2020, 10, 5673 conditions [4], providing driver tips to improve their driving comfort and safety [5], recognizing traffic activity and behavior [6], and detecting risky driving conditions [7]
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