[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121191-en":3,"doc-seo-121191-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121191,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Evaluation method of Driver’s olfactory preferences - a machine learning model based on multimodal physiological signals","Assessing drivers’ olfactory preferences supports improved in-vehicle odor environments and better driving comfort, yet existing evaluation approaches often rely on subjective ratings or specialized instrumentation with limited practicality. This study develops a machine learning classification framework using physiological signals captured in real driving environments. A dataset of 132 samples from 33 drivers includes heart-rate variability, electrodermal activity, and respiratory features processed to reduce environmental and individual differences. Six model types are trained and evaluated.","TYPE Original Research PUBLISHED 18 December 2024 DOI 10.3389/fbioe.2024.1433861  \nOPEN ACCESS  \nEDITED BY  \nYu-Feng Yu,  \nGuangzhou University, China  \nREVIEWED BY  \nHao Zhuang,  \nUniversity of California, Berkeley, United States Hatice Kose,  \nIstanbul Technical University, Türkiye José Antonio De La O Serna,  \nAutonomous University of Nuevo León, Mexico  \n*CORRESPONDENCE  \nZhian Hu,  \n [zhianhu@aliyun.com](zhianhu@aliyun.com)  \nRECEIVED 16 May 2024  \nACCEPTED 02 December 2024  \nPUBLISHED 18 December 2024  \nCITATION  \nTang B, Zhu M, Hu Z, Ding Y, Chen S and Li Y (2024) Evaluation method of Driver’s olfactory preferences: a machine learning model based on multimodal physiological signals.  \nFront. Bioeng. Biotechnol. 12:1433861 .  \ndoi: 10.3389/fbioe.2024.1433861  \nCOPYRIGHT  \n© 2024 Tang, Zhu, Hu, Ding, Chen and Li. This isan 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) and the copyright owner(s) 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.  \nEvaluation method of Driver’solfactory preferences: a machine learning model based on multimodal physiological signals  \nBangbei Tang 1,2, Mingxin Zhu 1,3, Zhian Hu 2*, Yongfeng Ding 1, Shengnan Chen 1 and Yan Li 1  \n1School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing, China, 2Department of Physiology, Army Medical University, Chongqing, China, 3School of Mechanical Engineering, Sichuan University of Science & Engineering, Yibin, Sichuan, China  \nIntroduction: Assessing the olfactory preferences of drivers can help improve theodor 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.  \nMethods: 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, KNearest Neighbors, and Naive Bayes) are trained and evaluated on this dataset.  \nResults: 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 by6 .33% on the dataset after baseline processing.  \nConclusions: 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.  \nKEYWORDS  \ndriving comfort, in-vehicle fragrance, olfactory preference, physiological signal, machine learning  \n1 Introduction  \nDriving comfort was a critical consideration in automotive design, prompting automakers to enhance driving comfort by using in-vehicle fragrances to improve theodor environment (Mustafa et al., 2016; Gentner et al., 2021) . However, the current evaluation methods were unable to effectively assess the olfactory pr","cbCaikEQFZqCwvju","https://ap.wps.com/l/cbCaikEQFZqCwvju","pdf",2498817,1,12,"English","en",105,"# Introduction\n## Related evaluation methods\n# Methods\n## Data collection and preprocessing\n## Machine learning models\n# Results\n## Classification performance comparison\n# Conclusions","[{\"question\":\"Why are drivers’ olfactory preferences important for driving comfort?\",\"answer\":\"Evaluating olfactory preferences helps improve the in-vehicle odor environment and enhances comfort during driving, which matters in automotive design.\"},{\"question\":\"How is the machine learning model trained and what data does it use?\",\"answer\":\"The model is trained on 132 olfactory preference samples from 33 drivers collected in real driving environments. It uses heart-rate variability, electrodermal activity, and respiratory signal features with baseline processing to reduce environmental and individual differences.\"},{\"question\":\"Which model performs best and how does baseline processing affect accuracy?\",\"answer\":\"All models can classify olfactory preferences, and the decision tree model achieves the highest accuracy (88%) with an F1-score of 0.87. Compared with data without baseline processing, accuracy increases by 3.50% and F1-score increases by 6.33% after baseline processing.\"}]","Evaluation method of Driver’s olfactory preferences - a machine learning model based on multimodal physiological signals | PDF",1785734282,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"evaluation-method-of-drivers-olfactory-preferences-a-machine-learning-model-based-on-multimodal-physiological-signals","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/evaluation-method-of-drivers-olfactory-preferences-a-machine-learning-model-based-on-multimodal-physiological-signals/121191/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are drivers’ olfactory preferences important for driving comfort?","Question",{"text":75,"@type":76},"Evaluating olfactory preferences helps improve the in-vehicle odor environment and enhances comfort during driving, which matters in automotive design.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the machine learning model trained and what data does it use?",{"text":80,"@type":76},"The model is trained on 132 olfactory preference samples from 33 drivers collected in real driving environments. It uses heart-rate variability, electrodermal activity, and respiratory signal features with baseline processing to reduce environmental and individual differences.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performs best and how does baseline processing affect accuracy?",{"text":84,"@type":76},"All models can classify olfactory preferences, and the decision tree model achieves the highest accuracy (88%) with an F1-score of 0.87. 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