Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals
The combination of physiological signals and machine learning models can effectively classify drivers' olfactory preferences. Results of this study can provide a comprehensive understanding on the olfactory preferences of drivers, ultimately enhancing driving comfort.
Authors
- Yongfeng Ding (ORCID: https://orcid.org/0000-0003-0211-4880)
- Mingxin Zhu (ORCID: https://orcid.org/0000-0002-0985-3857)
- Zhian Hu (ORCID: https://orcid.org/0000-0002-4439-537X)
- Bangbei Tang (ORCID: https://orcid.org/0000-0002-3870-0737)
- Shengnan Chen (ORCID: https://orcid.org/0000-0003-0899-1868)
- Yan Li (ORCID: https://orcid.org/0009-0006-4965-0372)
Institutions
- Army Medical University (CN)
- Chongqing University of Arts and Sciences (CN)
- Sichuan University of Science and Engineering (CN)
Publication Details
- Journal
- Frontiers in Bioengineering and Biotechnology
- Published
- 2024-12-18
- DOI
- https://doi.org/10.3389/fbioe.2024.1433861
- Citations
- 1
- Primary Topic
- Color perception and design
- Type
- article
- Field-Weighted Citation Impact
- 0.64
Funders
- National Natural Science Foundation of China
- Chongqing Municipal Education Commission