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

Journal: Frontiers in bioengineering and biotechnology
Published Date: Dec 18, 2024

Abstract

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.

Authors

  • Bangbei Tang
    School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing, China.
  • Mingxin Zhu
    School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing, China.
  • Zhian Hu
    Department of Physiology, Army Medical University, Chongqing, China.
  • Yongfeng Ding
    School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing, China.
  • Shengnan Chen
    School of Intelligent Manufacturing Engineering, Chongqing University of Arts and Sciences, Chongqing, China.
  • Yan Li
    Interdisciplinary Research Center for Biology and Chemistry, Liaoning Normal University, Dalian, China.

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