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Article

Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

1
Department of Neurosciences, Imaging and Clinical Sciences (DNISC), University G. d’Annunzio of Chieti-Pescara, 66100 Chieti, Italy
2
Next2U s.r.l., 65127 Pescara, Italy
*
Author to whom correspondence should be addressed.
Appl. Sci. 2020, 10(16), 5673; https://doi.org/10.3390/app10165673
Received: 14 July 2020 / Revised: 10 August 2020 / Accepted: 13 August 2020 / Published: 15 August 2020
(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)
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%. View Full-Text
Keywords: driver stress state; IR imaging; machine learning; support vector machine (SVR); advanced driver-assistance systems (ADAS) driver stress state; IR imaging; machine learning; support vector machine (SVR); advanced driver-assistance systems (ADAS)
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MDPI and ACS Style

Cardone, D.; Perpetuini, D.; Filippini, C.; Spadolini, E.; Mancini, L.; Chiarelli, A.M.; Merla, A. Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal. Appl. Sci. 2020, 10, 5673. https://doi.org/10.3390/app10165673

AMA Style

Cardone D, Perpetuini D, Filippini C, Spadolini E, Mancini L, Chiarelli AM, Merla A. Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal. Applied Sciences. 2020; 10(16):5673. https://doi.org/10.3390/app10165673

Chicago/Turabian Style

Cardone, Daniela, David Perpetuini, Chiara Filippini, Edoardo Spadolini, Lorenza Mancini, Antonio M. Chiarelli, and Arcangelo Merla. 2020. "Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal" Applied Sciences 10, no. 16: 5673. https://doi.org/10.3390/app10165673

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