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| Investigation on Driver Fatigue Based on WBAN and Approximate Entropy of Multi-Physiological Signals |
| Wang Lin1,2*, Fu Rongrong3, Zhang Chen1, Yin Xiaowei1, Hua Chengcheng2, Wang Hong2 |
1Department of Mechanical Engineering, Shenyang Institute of Engineering, Shenyang 110136, China
2School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China
3Measurement Technology and Instrumentation Key Lab of Hebei Province, Yanshan University, Qinhuangdao 066004, Hebei, China |
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Abstract Aimed to reasonably evaluate driver fatigue in driving process, three kinds of physiological signals, including electroencephalograph (EEG), electromyography (EMG) and respiration (RESP) signals of 12 subjects are recorded by wireless body area network (WBAN). Then, the approximate entropy (ApEn) of the signals are investigated during the driving process. The experimental results show that, ApEn of EEG, EMG, and RESP decrease in driving process. After about 90 min, the ApEn stays at a certain range of value, indicating the deeply driver fatigue. From principle components analysis, the contributions of the first two components are 47.33% and 40.26% (the total is more than 85%), and the weight of EEG and EMG is higher than RESP. From the statistical analysis, the values of P of ApEn of EEG and EMG are lower than 0.05, indicating EEG and EMG have better discrimination on driver fatigue. In case of the combination of EEG-EMG, there is obvious discrimination for the probability distribution of normal and fatigued state. This combination can effectively evaluate the fatigue degree during driving. Therefore, an optimized combination of physiological signals may be obtained, which is reasonable and reliable to evaluate the physiological characteristics of driver fatigue. The research results of present work can give a guidance to evaluate and relieve the driver fatigue.
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Received: 01 August 2016
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