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

• DOI: 10.3389/fbioe.2024.1433861
• Publication Date: 2024-12-18T06:44:09Z
• Authors: Bangbei Tang; Mingxin Zhu; Zhian Hu; Yongfeng Ding; Shengnan Chen; Yan Li
• Keywords: machine learning; 0302 clinical medicine; in-vehicle fragrance; physiological signal; 0202 electrical engineering, electronic engineering, information engineering; Bioengineering and Biotechnology; driving comfort; TP248.13-248.65; olfactory preference; Biotechnology

Assessing the olfactory preferences of drivers can help improve odor environment and enhance comfort during driving. However, current evaluation methods have limited availability, including subjective evaluation, electroencephalogram, behavioral action methods. Therefore, this study explores potential autonomic response signals for assessing preferences.