Study On Driving Fatigue Recognition Method Based On ECG Signal

With the increase in vehicle ownership,driving safety has attracted increasing attention.As an important part of the transportation system,the driving state directly determines the safety level of the whole transportation system.In many factors that affect the driver's state,driving fatigue is the most common.Therefore,for enhancing the level of traffic safety and reduce the accident rate,it's a great significance to detect the driving condition of the driver in real time,and timely warn in the event of fatigue.In order to solve the above problems,this paper builds a driving fatigue recognition model based on ECG signal.The main contents of this paper include the following points:First,the background and significance of driving fatigue testing are introduced,and the latest progress of research on research objects,research methods and research conclusions on the domestic and foreign is analyzed,and then the research content and technical route of this paper are presented,and then the research content and technical route of this paper are presented.Secondly,the mechanism of driving fatigue was discussed from the point of view of cognitive psychology,and the inducing factors and fatigue characteristics of driver fatigue were analyzed.The treatment method and index extraction theory of ECG signal are introduced in detail.The method of extraction of R-R interval was established by analyzing the heart rate variability and R-R interval.The method was validated by data,which laid a theoretical foundation for further extraction of ECG indicators which can effectively characterize the driver's physiological state.The third,we designed a dual-task model of long-term simulation driving test,which the main task for the car to follow,and the second task is the brake signal button response.By analyzing the ECG and behavior data collected in real time,it was found that with the occurrence of fatigue,the ECG index and behavior index showed a certain trend,and after significant analysis,there was a significant difference between the most early and late indicators.Therefore,it can be initially judged which the ECG indicator was better on driving fatigue.The fourth,expounded the feasibility of the reaction time as a division of fatigue grade.By analyzing the variation rule of simple reaction time in the whole experiment process,it is proposed to divide the fatigue grade by reaction time.First,the experimental process is divided into several periods,the driving state of the first period of time is mild fatigue state,through the significance analysis,the remaining time driving state calibration,distinguish between the two kinds of fatigue state.In addition,through the correlation analysis with the reaction time,the ECG indexes which can effectively reflect the fatigue state were extracted,and the ECG index group was identified.At last,based on SVM theory,the fatigue recognition model was constructed.By continuously adjusting the fatigue recognition of ECG index set and kernel function,the model recognition effect is analyzed.When the time domain and frequency domain indexes and RBF kernel function were comprehensive choice,the recognition effect of the model was optimal.Finally,the validity of the proposed model is verified by experimental data.