Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals
Bangbei Tang, Mingxin Zhu, Zhian Hu, Yongfeng Ding, Shengnan Chen, Yan Li · 2024
Read Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals by Bangbei Tang, Mingxin Zhu, Zhian Hu, Yongfeng Ding, Shengnan Chen, Yan Li (2024) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-11688291.txt.
Description
Bangbei Tang and co-authors try to read a driver's taste in car scents from the body's signals rather than from questionnaires. Thirty-three drivers gave 132 scent preference samples during real driving. Sensors captured heart rhythm patterns, skin conductance, and breathing, and the team cleaned the signals to remove each person's baseline.
Six standard learning algorithms were trained to sort drivers into scent preference classes. All of them worked, and a simple decision tree did best, reaching 88 percent accuracy. Cleaning the signals first raised accuracy by about three and a half points. The authors argue that body signals plus learning models offer a workable way to judge scent comfort, which could guide cabin fragrance design.
Catalog record
| CREATOR | Bangbei Tang, Mingxin Zhu, Zhian Hu, Yongfeng Ding, Shengnan Chen, Yan Li |
|---|---|
| YEAR | 2024 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 38 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |