Abstract
In present work, the heart rate variability (HRV) characteristics, calculated by sample entropy (SampEn), were used to analyze the driving fatigue state at successive driving stages. Combined with the relative power spectrum ratio β/(θ + α), subjective questionnaire, and brain network parameters of electroencephalogram (EEG) signals, the relationships between the different characteristics for driving fatigue were discussed. Thus, it can conclude that the HRV characteristics (RR SampEn and R peaks SampEn), as well as the relative power spectrum ratio β/(θ + α) of the channels (C3, C4, P3, P4), the subjective questionnaire, and the brain network parameters, can effectively detect driving fatigue at various driving stages. In addition, the method for collecting ECG signals from the palm part does not need patch electrodes, is convenient, and will be practical to use in actual driving situations in the future.
Highlights
Driver fatigue is one of the major causes of fatal road accidents according to the analysis of traffic incidents causation [1,2]
The former method, which determines the driver fatigue state mainly according to drivers’ and researchers’ judgments [4,5], is affected by drivers’ and researchers’ artificial subjective judgment errors. It is generally used as an auxiliary method for detecting driving fatigue, while for the latter, which mainly involves extracting and analyzing characteristics of EEG [6,7,8,9,10], electromyogram (EMG) [11], electrocardiogram (ECG) [12,13], electrooculogram (EOG) [14], visual characteristics [15], and facial movement, it is widely used in the detection of driver fatigue [16]
sample entropy (SampEn) is a modification of approximate entropy (ApEn), which is more reliable for short data
Summary
Driver fatigue is one of the major causes of fatal road accidents according to the analysis of traffic incidents causation [1,2]. Researchers have investigated different methods for detecting the fatigue state, which fall into the subjective method and the objective method The former method, which determines the driver fatigue state mainly according to drivers’ and researchers’ judgments [4,5], is affected by drivers’ and researchers’ artificial subjective judgment errors. It is generally used as an auxiliary method for detecting driving fatigue, while for the latter, which mainly involves extracting and analyzing characteristics of EEG [6,7,8,9,10], electromyogram (EMG) [11], electrocardiogram (ECG) [12,13], electrooculogram (EOG) [14], visual characteristics [15], and facial movement, it is widely used in the detection of driver fatigue [16]. Researchers have been devoted to the objective method for analyzing driver fatigue
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