Detecting driving stress in physiological signals based on multimodal feature analysis and kernel classifiers
作者：
Highlights：
• Automatic driving stress detection system in physiological records was proposed.
• Features were extracted from multimodal analysis.
• Efficient feature selection and reduction methods were employed.
• Several kernel-based classifiers were adopted and compared.
• Our proposed method shows a promising application in intelligent vehicle systems.
摘要
•Automatic driving stress detection system in physiological records was proposed.•Features were extracted from multimodal analysis.•Efficient feature selection and reduction methods were employed.•Several kernel-based classifiers were adopted and compared.•Our proposed method shows a promising application in intelligent vehicle systems.
论文关键词：Driving stress,Physiological signals,Multimodal feature extraction,Feature selection,Kernel-based classifiers
论文评审过程：Received 25 October 2016, Revised 13 December 2016, Accepted 25 January 2017, Available online 1 March 2017, Version of Record 23 May 2017.
论文官网地址：https://doi.org/10.1016/j.eswa.2017.01.040
