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Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning
Name
1907.09929.pdf
Description
Submitted version
Size
434.39 KB
Format
Adobe PDF
Checksum (MD5)
ccb1f48398309a0ebe86f70d14f6946a
Author(s)
Lopez-Martinez, Daniel
El-Haouij, Neska
Picard, Rosalind
Date Issued
2019
Journal
2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2019
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Lopez-Martinez, Daniel, El-Haouij, Neska and Picard, Rosalind. 2019. "Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning." 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2019.
Version
Original manuscript
Abstract
© 2019 IEEE. Affective states have a critical role in driving performance and safety. They can degrade driver situation awareness and negatively impact cognitive processes, severely diminishing road safety. Therefore, detecting and assessing drivers' affective states is crucial in order to help improve the driving experience, and increase safety, comfort and well-being. Recent advances in affective computing have enabled the detection of such states. This may lead to empathic automotive user interfaces that account for the driver's emotional state and influence the driver in order to improve safety. In this work, we propose a multiview multi-task machine learning method for the detection of driver's affective states using physiological signals. The proposed approach is able to account for inter-drive variability in physiological responses while enabling interpretability of the learned models, a factor that is especially important in systems deployed in the real world. We evaluate the models on three different datasets containing real-world driving experiences. Our results indicate that accounting for drive-specific differences significantly improves model performance.
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
10.1109/ACIIW.2019.8925190
