Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning
© 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...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/137058 |
| _version_ | 1811086845980704768 |
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| author | Lopez-Martinez, Daniel El-Haouij, Neska Picard, Rosalind |
| author_facet | Lopez-Martinez, Daniel El-Haouij, Neska Picard, Rosalind |
| author_sort | Lopez-Martinez, Daniel |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T13:35:36Z |
| format | Article |
| id | mit-1721.1/137058 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T13:35:36Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1370582021-11-03T03:19:15Z Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning Lopez-Martinez, Daniel El-Haouij, Neska Picard, Rosalind © 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. 2021-11-02T11:45:20Z 2021-11-02T11:45:20Z 2019 2021-07-06T14:09:28Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137058 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. en 10.1109/ACIIW.2019.8925190 2019 8th International Conference on Affective Computing and Intelligent Interaction Workshops and Demos, ACIIW 2019 Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) arXiv |
| spellingShingle | Lopez-Martinez, Daniel El-Haouij, Neska Picard, Rosalind Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning |
| title | Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning |
| title_full | Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning |
| title_fullStr | Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning |
| title_full_unstemmed | Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning |
| title_short | Detection of Real-World Driving-Induced Affective State Using Physiological Signals and Multi-View Multi-Task Machine Learning |
| title_sort | detection of real world driving induced affective state using physiological signals and multi view multi task machine learning |
| url | https://hdl.handle.net/1721.1/137058 |
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