Abstract
Driving fatigue is an important contributing factor to traffic crashes. Developing a system that monitors the driver's fatigue level in real time and produces alarm signals when necessary, is important for the prevention of accidents. In the past decades, many recognition algorithms were developed based on multiple indicators. However, the relationship between R-R intervals of ECG and driving fatigue has not been studied. We develop a model to recognize the driving fatigue state based on R-R intervals. The cluster effect in the R-R interval sequence is found based on the stationary test and ARCH effect test. Then the AR (1)-GARCH (1, 1) model is developed to fit the time sequence of R-R intervals. The conditional variance of the residual R-R sequence is used to recognize whether there are changes in driving states. Field data was collected on the freeway between Baicheng and Changchun cities, where 10 drivers took part in the experiments. Validations are conducted to test the effectiveness of the developed model, and the results show that the recognitions of driving fatigue for the 10 drivers are correct in all cases. In addition, the recognition time delay is smaller than 5 minutes.
Highlights
A driving fatigue state recognition algorithm is developed based on the fluctuation characteristics of the cluster effect, which can be obtained from the conditional variance of the residual R-R interval sequence
In this paper, the heart rate R-R interval sequence is chosen as an indicator to recognize the driving fatigue state
According to the case study based on field data, the developed model can recognize the driving fatigue state timely
Summary
A driving fatigue state recognition algorithm is developed based on the fluctuation characteristics of the cluster effect, which can be obtained from the conditional variance of the residual R-R interval sequence. In this algorithm, there is no need to set threshold values for recognition indexes, which overcomes the limitations of previous studies that divided the driving state into several levels according to the driver’s subjective feelings. L. Wang et al.: Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data. L. Wang et al.: Modeling and Recognition of Driving Fatigue State Based on R-R Intervals of ECG Data TABLE 2.
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