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A method for fatigue detection based on Driver's steering wheel grip
International Journal of Industrial Ergonomics ( IF 3.1 ) Pub Date : 2021-01-17 , DOI: 10.1016/j.ergon.2021.103083
Rui Li , Yingjie Victor Chen , Linghao Zhang

We propose a simple method to measure a driver's fatigue state by detecting the driver's grip force on the steering wheel while driving. We tested the grip force of 36 drivers on the steering wheel in conscious states (Alert) and fatigue states under actual road driving conditions. Using the Stanford sleepiness scale (SSS), we divided drivers into Alert Group A, fatigue Group A, and fatigue Group B. During 20-min real-road driving trials, we measured the steering wheel grip force, electroencephalogram index (R = (α + θ)/β), and blink frequency of each driver synchronously. We found that ΔF, the difference between the maximum/minimum grip force and the standard deviation of the grip force, σF, for each driver, strongly correlated with the driver's fatigue state. In the fatigue state, both ΔF and σF increased significantly. We examined these force indices using analysis of variance (ANOVA) and validated them against the R-value, blink frequency, and the driver's self-reported fatigue state. Using the grip force in fatigue detection, our method can achieve an overall recognition rate of 86.6% and an individual recognition rate of 88.3%. These results indicate that this method can effectively detect a driver's fatigue state during actual road driving. This new method has several advantages, such as a high signal-to-noise ratio, simple data collection, and no influence on daily driving. Thus, our proposed method may provide a theoretical foundation for the development of fatigue-detecting steering wheels



中文翻译:

一种基于驾驶员方向盘抓地力的疲劳检测方法

我们提出了一种简单的方法,通过在驾驶过程中检测驾驶员在方向盘上的抓握力来测量驾驶员的疲劳状态。我们测试了36个驾驶员在实际道路行驶条件下在有意识状态(警报)和疲劳状态下在方向盘上的抓地力。使用Stanford嗜睡量表(SSS),我们将驾驶员分为警报组A,疲劳组A和疲劳组B。在20分钟的实际道路驾驶试验中,我们测量了方向盘抓地力,脑电图指数(R =( α+θ)/β),并同步每个驱动器的闪烁频率。我们发现,每个驾驶员的最大/最小抓地力与抓地力的标准偏差σF之差ΔF与驾驶员的疲劳状态密切相关。在疲劳状态下,ΔF和σF均显着增加。我们使用方差分析(ANOVA)检查了这些力指数,并针对R值,眨眼频率和驾驶员自我报告的疲劳状态进行了验证。使用疲劳检测中的抓地力,我们的方法可以达到86.6%的整体识别率和88.3%的个人识别率。这些结果表明,该方法可以有效地检测实际道路驾驶中驾驶员的疲劳状态。这种新方法具有多个优点,例如高信噪比,简单的数据收集以及对日常驾驶没有影响。因此,我们提出的方法可以为疲劳检测方向盘的发展提供理论依据。使用疲劳检测中的抓地力,我们的方法可以实现86.6%的整体识别率和88.3%的个人识别率。这些结果表明,该方法可以有效地检测实际道路驾驶中驾驶员的疲劳状态。这种新方法具有多个优点,例如高信噪比,简单的数据收集以及对日常驾驶没有影响。因此,我们提出的方法可以为疲劳检测方向盘的发展提供理论依据。使用疲劳检测中的抓地力,我们的方法可以达到86.6%的整体识别率和88.3%的个人识别率。这些结果表明,该方法可以有效地检测实际道路驾驶中驾驶员的疲劳状态。这种新方法具有多个优点,例如高信噪比,简单的数据收集以及对日常驾驶没有影响。因此,我们提出的方法可以为疲劳检测方向盘的发展提供理论依据。例如高信噪比,简单的数据收集,并且不会影响日常驾驶。因此,我们提出的方法可以为疲劳检测方向盘的发展提供理论依据。例如高信噪比,简单的数据收集,并且不会影响日常驾驶。因此,我们提出的方法可以为疲劳检测方向盘的发展提供理论依据。

更新日期:2021-01-18
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