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.
Wang Lin,Wang Hong,Fu Rongrong, et al. Investigation on Actual Driver Fatigue Based on Combination of Multi-Characteristics[J]. Chinese Journal of Biomedical Engineering, 2023, 42(5): 554-562.
[1] Davidović J, Pešić D, Lipovac K, et al. The significance of the development of road safety performance indicators related to driver fatigue [J]. Transportation Research Procedia, 2020, 45: 333-342. [2] 付荣荣,鲍甜恬,田永胜,等.基于子成分分解的脑电信号去噪方法比较研究[J].计量学报,2019,40(4):708-713. [3] Schneider L,Frings K,Rothe S, et al. Effects of a seat-integrated mobilization system during passive driver fatigue [J]. Accident Analysis and Prevention, 2021, 150: 105883. [4] Hu Xinyun, Lodewijks G. Detecting fatigue in car drivers and aircraft pilots by using non-invasive measures: the value of differentiation of sleepiness and mental fatigue [J]. Journal of Safety Research, 2020, 72: 173-187. [5] Zhou Feng, Alsaid A, Blommer M, et al. Driver fatigue transition prediction in highly automated driving using physiological features [J]. Expert Systems with Applications, 2020, 147: 113204. [6] Ahmadi A, Bazregarzadeh H, Kazemi K. Automated detection of driver fatigue from electroencephalography through wavelet-based connectivity [J]. Biocybernetics and Biomedical Engineering, 2020, 41(1): 316-332. [7] 李昕,李红红,李长吾.基于复杂度熵特征融合的高压力人群情感状态评估[J].中国生物医学工程学报,2013,32(3):313-320. [8] 王波,唐先智,王连东,等.基于EEMD和熵理论的电动汽车制动意图识别方法[J].汽车工程,2018,40(8):935-941. [9] Patel M, Lal SKL, Kavanagh D, et al. Applying neural network analysis on heart rate variability data to assess driver fatigue [J]. Expert Systems with Applications, 2011, 38(6): 7235-7242. [10] Zhang Chi, Sun Lina, Cong Fengyu, et al. Optimal imaging of multi-channel EEG features based on a novel clustering technique for driver fatigue detection [J]. Biomedical Signal Processing and Control, 2020, 62: 102103. [11] 张启忠,席旭刚,马玉良,等.基于表面肌电信号的手腕动作模式识别[J].中国生物医学工程学报,2013,32(3):257-265. [12] Hostens I, Ramon H. Assessment of muscle fatigue in low level monotonous task performance during car driving [J]. Journal of Electromyography and Kinesiology, 2005, 15(3): 266-274. [13] Zou Shuli, Qiu Taorong, Huang Peifan, et al. Constructing multi-scale entropy based on the empirical mode decomposition (EMD) and its application in recognizing driving fatigue [J]. Journal of Neuroscience Methods, 2020, 341: 108691. [14] Luo Haowen, Qiu Taorong, Liu Chao, et al. Research on fatigue driving detection using forehead EEG based onadaptive multi-scale entropy [J]. Biomedical Signal Processing and Control, 2019, 51: 50-58. [15] Zhang Chi, Ma Jinfei, Zhao Jian, et al. Decoding analysis of alpha oscillation networks on maintaining driver alertness [J]. Entropy, 2020, 22(7): 22070787. [16] 张明恒,刘朝阳,郭政先,等.基于GM-HMM的驾驶人疲劳状态检测[J].大连理工大学学报,2021,61(4):399-407. [17] Wang Haijie, Liu Xintian, Wu Que, et al. An improved fatigue life prediction model for shock absorber cylinder with surface roughness correction [J]. Engineering Computations, 2021, 38(6): 2713-2732. [18] Xiao Cangyan, Han Liu, Chen Shuzhao. Automobile driver fatigue detection method based on facial image recognition under single sample condition [J]. Symmetry, 2021, 13(7): 1195-1195. [19] Fasee U, Zaka U, Sheeraz A, et al. Traffic priority based delay-aware and energy efficient path allocation routing protocol for wireless body area network [J]. Journal of Ambient Intelligence and Humanized Computing, 2019, 10(10): 3775-3794. [20] 樊凤杰,白洋,纪会芳.基于EEMD-ICA的脑电去噪算法研究[J].计量学报,2021,42(3):395-400. [21] 付荣荣,王宏,王琳,等.基于无线体域网中多生理信号驾驶疲劳检测[J].东北大学学报(自然科学版),2014,35(6):850-853. [22] 谢宏,周笑丽,夏斌,等.疲劳驾驶识别中的脑电信号特征选择算法和支持向量机模型研究[J].中国生物医学工程学报,2014,33(4):482-486. [23] Richman JS, Moorman JR. Physiological time-series analysis using approximate entropy and sample entropy [J]. American Journal of Physiology Heart & Circulatory Physiology, 2000, 278(6): 2039-2049. [24] Sabeti M, Katebi S, Boostani R. Entropy and complexity measures for EEG signal classification of schizophrenic and control participants [J]. Artificial Intelligence in Medicine, 2009, 47(3): 263-274. [25] 王琳,化成城,姜鑫,等.基于颈腰部肌电及脑电信号的疲劳驾驶检测[J].东北大学学报(自然科学版),2018,39(1):102-107. [26] Askari S. Fuzzy C-means clustering algorithm for data with unequal cluster sizes and contaminated with noise and outliers: review and development [J]. Expert Systems with Applications, 2021, 165: 113856. [27] Kobayashi Y. Improved method for correcting sample Mahalanobis distance without estimating population eigenvalues or eigenvectors of covariance matrix [J]. International Journal of Data Science & Analytics, 2020, 10(2): 121-134. [28] Wang Hong, Zhang Chi, Shi Tianwei, et al. Real-time EEG-based detection of fatigue driving danger for accident prediction [J]. International Journal of Neural Systems, 2015, 25(2): 155-164. [29] Zhang Chi, Wang Hong, Fu Rongrong. Automated detection of driver fatigue based on entropy and complexity measures [J]. IEEE Transactions on Intelligent Transportation Systems, 2014, 15(1): 168-177. [30] 高明星,杨春宇,朱守林.草原公路长时程实驾试验驾驶员颈部疲劳特性分析[J].内蒙古农业大学学报(自然科学版),2020,41(4):51-58. [31] 张驰.基于脑/肌/眼电的疲劳驾驶检测技术的研究[D].沈阳:东北大学,2012. [32] 包萨日娜.草原公路行车驾驶员颈部疲劳试验研究[D].呼和浩特:内蒙古农业大学,2015. [33] 叶成文.基于心电肌电信号的汽车驾驶疲劳研究[D].合肥:合肥工业大学,2018. [34] 谢平,齐孟松,张园园,等.基于多生理信息及迁移学习的驾驶疲劳评估[J].仪器仪表学报,2018,39(10):223-231. [35] 王永祥.基于EEG的疲劳检测技术的研究与应用[D].南京:南京邮电大学,2019. [36] 王星.基于脑电信号的疲劳驾驶检测研究[D].杭州:杭州电子科技大学,2019. [37] Murata A. Proposal of a method to predict subjective rating on drowsiness using physiological and behavioral measures [J]. IIE Transaction on Occupational Ergonomics & Human Factors, 2016, 4(2): 128-140. [38] Laurent F, Valderrama M, Besserve M, et al. Multimodal information improves the rapid detection of mental fatigue [J]. Biomedical Signal Processing and Control, 2013, 8(4): 400-408. [39] Huo Xueqin, Zheng Weilong, Lu Baoliang. Driving fatigue detection with fusion of EEG and forehead EOG [C]//IEEE International Joint Conference on Neural Networks (IJCNN). Vancouver: IEEE, 2016: 897-904. [40] Wei L, Mukhopadhyay SC, Jidin R, et al. Multi-source information fusion for drowsy driving detection based on wireless sensor networks [C]// IEEE Seventh International Conference on Sensing Technology. Wellington: IEEE, 2013: 861-868. [41] Janjarasjitt S, Scher MS, Loparo KA. Nonlinear dynamical analysis of the neonatal EEG time series: the relationship between sleep state and complexity [J]. Clinical Neurophysiology, 2008, 119(8): 1812-1823. [42] 刘加海,王健,罗晓芳.局部肌肉疲劳的表面肌电信号复杂度和熵变化[J].生物物理学报,2004,20(3):198-202. [43] 谢智.基于生理信号的驾驶疲劳模型研究[D].苏州:苏州大学,2017.
