Cross-subject driver status detection from physiological signals based on hybrid feature selection and transfer learning
作者：
Highlights：
• Cross-subject learning framework for driver status detection was proposed.
• Features were estimated from both class separation and domain fusion perspectives.
• Hybrid feature selection method based on multiple filter algorithms was employed.
• Several domain adaptation transfer learning techniques were adopted and compared.
• Both simulated and real drive datasets verified the effectiveness of proposed method.
摘要
•Cross-subject learning framework for driver status detection was proposed.•Features were estimated from both class separation and domain fusion perspectives.•Hybrid feature selection method based on multiple filter algorithms was employed.•Several domain adaptation transfer learning techniques were adopted and compared.•Both simulated and real drive datasets verified the effectiveness of proposed method.
论文关键词：Driver status,Physiological signals,Cross-subject feature evaluation,Hybrid feature selection,Transfer learning
论文评审过程：Received 7 June 2018, Revised 31 January 2019, Accepted 2 February 2019, Available online 4 February 2019, Version of Record 8 July 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.02.005
