Detecting Thermal Discomfort of Drivers Using Physiological Sensors and Thermal Imaging

• Published in: IEEE intelligent systems Vol. 34; no. 5; pp. 3 - 13
• Main Authors: Abouelenien, Mohamed, Burzo, Mihai
• Format: Journal Article
• Language: English
• Published: Los Alamitos IEEE 01.09.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Automatic control; Discomfort; Feature extraction; Heat detection; Intelligent systems; Machine learning; Physiology; Sensors; Temperature sensors; Thermal comfort; Thermal imaging
• ISSN: 1541-1672; 1941-1294
• DOI: 10.1109/MIS.2019.2938713

Recent technological developments have been used extensively in manufacturing vehicles in order to improve the driving experience and add multiple safety features. This article introduces a novel machine learning approach using physiological sensors and thermal imaging of the subjects to detect human thermal discomfort in order to develop a fully automated climate control system in the vehicles that does not need any explicit input from individuals. To achieve this goal, a dataset of thermal videos and physiological signals from 50 subjects is collected, an extensive analysis of different feature sets is conducted, a multimodal approach is experimented, and a cascaded classification system is proposed. Our results evidently show the capability of specific feature sets of detecting human thermal discomfort as well as the superior performance of integrating multimodal features.