Physiological signal based detection of driver hypovigilance using higher order spectra
作者：
Highlights：
• ECG and EMG signals collected and these physiological signals are preprocessed.
• The hypovigilance features extracted from signals using HOS feature.
• The features were classified using KNN, LDA and QDA.
• Maximum accuracy of 96.75% and 92.31% for ECG and EMG signals, respectively.
• Feature of ECG and EMG were fused with PCA and the classification accuracy was 96%.
摘要
•ECG and EMG signals collected and these physiological signals are preprocessed.•The hypovigilance features extracted from signals using HOS feature.•The features were classified using KNN, LDA and QDA.•Maximum accuracy of 96.75% and 92.31% for ECG and EMG signals, respectively.•Feature of ECG and EMG were fused with PCA and the classification accuracy was 96%.
论文关键词：ECG,EMG,Physiological measures,Driver drowsiness,Driver distraction
论文评审过程：Received 12 January 2015, Revised 10 July 2015, Accepted 12 July 2015, Available online 21 July 2015, Version of Record 29 August 2015.
论文官网地址：https://doi.org/10.1016/j.eswa.2015.07.021
