Wang Fuwang, Lu Bin, Kang Xiaogang, Fu Rongrong
School of Mechanic Engineering, Northeast Electric Power University, Jilin 132012, China.
College of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China.
Entropy (Basel). 2021 Sep 14;23(9):1209. doi: 10.3390/e23091209.
The accurate detection and alleviation of driving fatigue are of great significance to traffic safety. In this study, we tried to apply the modified multi-scale entropy (MMSE) approach, based on variational mode decomposition (VMD), to driving fatigue detection. Firstly, the VMD was used to decompose EEG into multiple intrinsic mode functions (IMFs), then the best IMFs and scale factors were selected using the least square method (LSM). Finally, the MMSE features were extracted. Compared with the traditional sample entropy (SampEn), the VMD-MMSE method can identify the characteristics of driving fatigue more effectively. The VMD-MMSE characteristics combined with a subjective questionnaire (SQ) were used to analyze the change trends of driving fatigue under two driving modes: normal driving mode and interesting auditory stimulation mode. The results show that the interesting auditory stimulation method adopted in this paper can effectively relieve driving fatigue. In addition, the interesting auditory stimulation method, which simply involves playing interesting auditory information on the vehicle-mounted player, can effectively relieve driving fatigue. Compared with traditional driving fatigue-relieving methods, such as sleeping and drinking coffee, this interesting auditory stimulation method can relieve fatigue in real-time when the driver is driving normally.
准确检测和缓解驾驶疲劳对交通安全具有重要意义。在本研究中,我们尝试将基于变分模态分解(VMD)的改进多尺度熵(MMSE)方法应用于驾驶疲劳检测。首先,使用VMD将脑电图(EEG)分解为多个固有模态函数(IMF),然后使用最小二乘法(LSM)选择最佳的IMF和尺度因子。最后,提取MMSE特征。与传统的样本熵(SampEn)相比,VMD-MMSE方法能够更有效地识别驾驶疲劳的特征。将VMD-MMSE特征与主观问卷(SQ)相结合,分析了正常驾驶模式和有趣听觉刺激模式两种驾驶模式下驾驶疲劳的变化趋势。结果表明,本文采用的有趣听觉刺激方法能够有效缓解驾驶疲劳。此外,这种简单地在车载播放器上播放有趣听觉信息的有趣听觉刺激方法,能够有效缓解驾驶疲劳。与传统的缓解驾驶疲劳方法,如睡觉和喝咖啡相比,这种有趣听觉刺激方法能够在驾驶员正常驾驶时实时缓解疲劳。