Purpose–This study aims to analyze the differences of electrocardiograph(ECG)characteristics for female drivers in calm and anxious states during driving.Design/methodology/approach–The authors used various material...Purpose–This study aims to analyze the differences of electrocardiograph(ECG)characteristics for female drivers in calm and anxious states during driving.Design/methodology/approach–The authors used various materials(e.g.visual materials,auditory materials and olfactory materials)to induce drivers’mood states(calm and anxious),and then conducted the real driving experiments and driving simulations to collect driver’s ECG signal dynamic data.Physiological changes in ECG during the stimulus process were recorded using PSYLAB software.The paired T-test analysis was conducted to determine if there is a significant difference in driver’s ECG characteristics between calm and anxious states during driving.Findings–The results show significant differences in the characteristic parameters of female driver’s ECG signals,including(average heart rate),(atrioventricular interval),(percentage of NN intervals>50ms),(R wave average peak),(Root mean square of successive),(Q wave average peak)and(S wave average peak),in time domain,frequency domain and waveform in emotional states of calmness and anxiety.Practical implications–Findings of this work show that ECG can be used to identify driver’s anxious and calm states during driving.It can be used for the development of personalized driver assistance system and driver warning system.Originality/value–Only a few attempts have been made on the influence of human emotions on physiological signals in the transportationfield.Hence,there is a need for transport scholars to begin to identify driver’s ECG characteristics under different emotional states.This study will analyze the differences of ECG characteristics for female drivers in calm and anxious states during driving to provide a theoretical basis for developing the intelligent and connected vehicles.展开更多
基金supported by the Joint Laboratory for Internet of Vehicles,Ministry of Education–China Mobile Communications Corporation under Project[Grant No.ICV-KF2018-03]Qingdao Top Talent Program of Entrepreneurship and Innovation(Grant No.19-3-2-8-zhc)+1 种基金the National Natural Science Foundation of China(Grant Nos.71901134,61074140,61573009,51508315)the Natural Science Foundation of Shandong Province(Grant No.ZR2017LF015).
文摘Purpose–This study aims to analyze the differences of electrocardiograph(ECG)characteristics for female drivers in calm and anxious states during driving.Design/methodology/approach–The authors used various materials(e.g.visual materials,auditory materials and olfactory materials)to induce drivers’mood states(calm and anxious),and then conducted the real driving experiments and driving simulations to collect driver’s ECG signal dynamic data.Physiological changes in ECG during the stimulus process were recorded using PSYLAB software.The paired T-test analysis was conducted to determine if there is a significant difference in driver’s ECG characteristics between calm and anxious states during driving.Findings–The results show significant differences in the characteristic parameters of female driver’s ECG signals,including(average heart rate),(atrioventricular interval),(percentage of NN intervals>50ms),(R wave average peak),(Root mean square of successive),(Q wave average peak)and(S wave average peak),in time domain,frequency domain and waveform in emotional states of calmness and anxiety.Practical implications–Findings of this work show that ECG can be used to identify driver’s anxious and calm states during driving.It can be used for the development of personalized driver assistance system and driver warning system.Originality/value–Only a few attempts have been made on the influence of human emotions on physiological signals in the transportationfield.Hence,there is a need for transport scholars to begin to identify driver’s ECG characteristics under different emotional states.This study will analyze the differences of ECG characteristics for female drivers in calm and anxious states during driving to provide a theoretical basis for developing the intelligent and connected vehicles.
