Detecting Thermal Discomfort of Drivers Using Physiological Sensors and Thermal Imaging

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 huma...

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Bibliographic Details
Published inIEEE intelligent systems Vol. 34; no. 5; pp. 3 - 13
Main Authors Abouelenien, Mohamed, Burzo, Mihai
Format Journal Article
LanguageEnglish
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
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Summary: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.
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ISSN:1541-1672
1941-1294
DOI:10.1109/MIS.2019.2938713