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ORIGINAL RESEARCH article

Front. Bioeng. Biotechnol.
Sec. Biosensors and Biomolecular Electronics
Volume 12 - 2024 | doi: 10.3389/fbioe.2024.1433861
This article is part of the Research Topic Explainable Models in Biosensors, Biosensing Technology, and Biomedical Engineering View all articles

Evaluation Method of Driver's Olfactory Preferences: A Machine Learning Model Based on Multimodal Physiological Signals

Provisionally accepted
Bangbei Tang Bangbei Tang 1,2Mingxin Zhu Mingxin Zhu 2,3Zhian Hu Zhian Hu 1*Yongfeng Ding Yongfeng Ding 2Shengnan Chen Shengnan Chen 2Yan Li Yan Li 2
  • 1 Army Medical University, Chongqing, China
  • 2 Chongqing University of Arts and Sciences, Chongqing, China
  • 3 Sichuan University of Science and Engineering, Zigong, Sichuan, China

The final, formatted version of the article will be published soon.

    Assessing the olfactory preferences of drivers can help improve the odor environment and enhance comfort during driving. However, the current evaluation methods have limited availability, including subjective evaluation, electroencephalogram, and behavioral action methods. Therefore, this study explores the potential of autonomic response signals for assessing the olfactory preferences. This paper develops a machine learning model that classifies the olfactory preferences of drivers based on physiological signals. The dataset used for training in this study comprises 132 olfactory preference samples collected from 33 drivers in real driving environments. The dataset includes features related to heart rate variability (HRV), electrodermal activity (EDA), and respiratory signals (RESP) which are baseline processed to eliminate the effects of environmental and individual differences. Six types of machine learning models (Logistic Regression, Support Vector Machine, Decision Tree, Random Forest, K-Nearest Neighbors, and Naive Bayes) are trained and evaluated on this dataset. The results demonstrate that all models can effectively classify driver olfactory preferences, and the decision tree model achieves the highest classification accuracy (88%) and F1-score (0.87). Additionally, compared with the dataset without baseline processing, the model's accuracy increases by 3.50%, and the F1-score increases by 6.33% on the dataset after baseline processing. Results of this study can provide a comprehensive understanding on the olfactory preferences of drivers, ultimately enhancing driving comfort.

    Keywords: Driving comfort, in-vehicle fragrance, Olfactory preference, Physiological signal, machine learning

    Received: 16 May 2024; Accepted: 02 Dec 2024.

    Copyright: © 2024 Tang, Zhu, Hu, Ding, Chen and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Zhian Hu, Army Medical University, Chongqing, 400038, China

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.