Method for predicting drunk driving and fatigue driving based on heart rate variability (HRV) and adversarial network
A technology for fatigue driving and networking, applied in the field of intelligent transportation, can solve problems such as inconvenience, loud ECG signal noise, and difficulty for drivers, and achieve the effect of easy acceptance and noise reduction.
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[0049] A method for predicting drunk driving and fatigue driving based on HRV and adversarial networks, such as figure 1 shown, including the following steps:
[0050] S1. Use the ECG sensor embedded in the steering wheel to measure the continuous ECG signal from the driver's palm;
[0051] S2. Use wavelet decomposition to denoise the ECG signal; the specific steps are as follows:
[0052] S21, denoise the original ECG signal obtained in step S1 based on 4th wavelet decomposition, and obtain wavelet coefficients;
[0053] S22, adopt soft threshold value algorithm to update wavelet coefficient, the similarity of the direction of wavelet is recorded as λ, specifically as follows:
[0054]
[0055] Where N is the number of signals, and σ is the standard deviation of the noise, which is estimated by the formula:
[0056]
[0057] where d s Represents the sth wavelet coefficient, median(|d s |) is the wavelet coefficient d s , the median value, set the adjustment coeffici...
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