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Study on the Mapping Law of the Fluctuation of Heart Rate Parameters and the Change of Driving State

机译:心率参数波动的映射规律及驾驶状态变化研究

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Under the influence of different external environment and its own factors, many drivers are prone to operate mistakes, which would lead to accidents. Thus, the main purpose of this paper is to analyze the driver's driving state by detecting the driver's heart rate fluctuation parameters. Firstly, wavelet transform was used to denoise the ECG signal of the driver when a driver was in the process of driving simulation. Then, the characteristic value of the ECG signal was extracted. Heart rate fluctuations (HRF) analysis from two different angles showed that the fluctuation of heart rate parameters could be obtained directly from the ECG signal, which was related to driving status. Through the comprehensive analysis of the ECG signal, the driving position was redefined as the relative position value of the vehicle to the road centerline during driving, and it was represented by a cloud model. The standard one-dimensional cloud model of drive state was obtained through Python programming. It was found that the expectations and variances of the one-dimensional cloud model may also be related to fluctuations in heart rate parameters. Lastly, the mapping relationship between them was obtained.
机译:在不同的外部环境和自身因素的影响下,许多司机都倾向于经营错误,这将导致事故。因此,本文的主要目的是通过检测驾驶员的心率波动参数来分析驾驶员的驾驶状态。首先,当驾驶员处于驾驶模拟过程中时,使用小波变换来表示驾驶员的ECG信号。然后,提取ECG信号的特征值。来自两个不同角度的心率波动(HRF)分析表明,可以直接从ECG信号获得心率参数的波动,这与驱动状态有关。通过对ECG信号的综合分析,在驾驶期间将驱动位置重新定义为车辆到道路中心线的相对位置值,并且由云模型表示。通过Python编程获得了驱动状态的标准一维云模型。结果发现,一维云模型的期望和差异也可能与心率参数的波动有关。最后,获得了它们之间的映射关系。

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