Evaluation method of Driver's olfactory preferences: a machine learning model based on multimodal physiological signals.

Introduction: 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.

Methods: 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, electrodermal activity, and respiratory signals 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.

Results: 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.

Conclusions: 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.