| Summary: | As the number of vehicles increases, the probability of fatigue driving rises accordingly, resulting in a boost in the accident rate. Based on principal component analysis, the paper presents a fatigue monitoring algorithm that integrates with facial, EEG, EMG and ECG. First of all, based on the edge detection method, the eyes are positioned and then it has an assessment through the Perclos fatigue. EEG, EMG, ECG indicators evaluate the fatigue with the corresponding fatigue characteristics. Subsequently, the related values of the above four indexes are normalized, and the principal component analysis method (PCA) is utilized to reduce the dimension and merge to get the comprehensive fatigue eigenvalue, and then we get the fatigue level referring to the fatigue level standard. Analyzing the measured data, the algorithm accuracy is 76%.
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