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| Investigation on Actual Driver Fatigue Based on Combination of Multi-Characteristics |
| Wang Lin1*, Wang Hong2, Fu Rongrong3, Yin Xiaowei1, Liu Jintao1 |
1(Department of Mechanical Engineering, Shenyang Institute of Engineering, Shenyang 110136, China)
2(School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China)
3(Measurement Technology and Instruction Key Lab of Hebei Province, Yanshan University, Qinhuangdao 066004, Hebei, China) |
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Abstract In order to discriminate driver fatigue accurately in real-time and reduce the traffic accidents caused by driver fatigue, physiological signals of 12 subjects in actual driving were recorded by wireless body area network (WBAN), and approximate entropy (ApEn) of electroencephalograph (EEG), electromyography (EMG) and respiration (RESP) signals were extracted. The upper trapeziuses at 2 cm of both sides of 6th spinous process were determined as the data acquisition positions of EMG based on distortion energy density (DED) theory. Then the discriminant degree of their combination was analyzed by the fuzzy C-clustering method. Finally, a discriminant model on driver fatigue was built based on Mahalanobis distance theory. The experimental results showed that the decreasing trend of the upper trapezius at 6th spinous process was more obvious than that at 7th spinous process, and the significant index P<0.05, indicating the muscles at 6th spinous process were more sensitive for driver fatigue. The actual testing result was consistent with the calculation result of DED theory, and verified the correctness of acquisition position of EMG. During the actual driving, the ApEns of EEG, EMG and RESP signals decreased. After about 90 min, the decreasing trend slowed down, indicating the deeper fatigue. By the fuzzy C-clustering analysis, in the case of the combination of EEG-EMG, obvious discrimination of the probability distribution between normal and fatigued state were detected, and they were selected as independent variables. Finally, a discriminant model on driver fatigue based on Mahalanobis distance theory was built, and its accuracy was up to 90.92%, which effectively discriminated the driver fatigue.
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Received: 18 October 2021
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Corresponding Authors:
*E-mail: wanglin@sie.edu.cn
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