Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals
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
DOI:
10.3389/fbioe.2024.1433861
Publication Date:
2024-12-18T06:44:09Z
AUTHORS (6)
ABSTRACT
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.
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