汽车驾驶负荷对驾驶员心脑功能连接的影响分析

发布时间：2018-10-12 11:26

【摘要】：近几年,汽车的保有量逐年增加,因此带来的安全问题备受关注。文献研究表明,驾驶员在驾驶过程中易突发脑中风等心脑血管疾病,其中高负荷驾驶是一个重要诱发因素。为了满足人们对汽车多功能性的需求,汽车人机交互界面越来越复杂,这无疑增加了驾驶员的驾驶负荷。对汽车驾驶室人机交互界面设计进行科学评价,成为当前汽车人机工程学领域需要解决的重要课题。因此,本文旨在从驾驶员心脑功能连接角度出发分析驾驶负荷,为人机交互界面优化设计提供可靠的理论依据。心脑功能连接状态可反映大脑在面对机体血压变化时调节脑血流的能力。驾驶员在应对高的驾驶负荷时,大脑耗氧量势必增加。为了确保脑细胞可以进行正常的代谢活动,就需要有充足的脑血流灌注。因此,通过研究驾驶员的心脑功能动态调整能力来评估驾驶负荷是目前可行的评价方法。对于脑血流自主调节(心脑功能动态调整)的分析,近红外光谱已被公认为是一种非常合适的技术。一方面,它不仅有好的时间分辨率(接近于毫秒),还有好的空间分辨率(5-10毫米),可以探测大脑组织微循环的氧合血红蛋白浓度变化,结合动脉血压的检测,就可以实现对大脑进行局域化的心脑功能连接评估。另一方面,基于近红外光谱原理检测的氧合代谢信号不容易受运动噪声的干扰,而且相应检测设备具有便携性,可以对驾驶员进行实时检测。本文首先通过设计警惕操作任务平台来模拟驾驶员在实际行车过程的驾驶负荷,然后利用近红外光谱设备采集驾驶员在静息态和警惕任务状态下前额叶区域和运动区域的脑血氧信号,同时利用血压仪采集驾驶员的动脉血压信号,最后基于Morlet、波的相干法分析上述两信号在特征低频段上的耦合关系,从而来评估驾驶负荷对驾驶员心脑功能连接的影响。研究发现,在警惕操作任务状态下,小波相干性和相位相干性在某些频率段上与静息态下的相比有显著差异。结果表明,驾驶负荷对心脑功能连接有明显影响。通过本文研究,可以得出：基于小波变化的方法可以从时频平面上展现更多的生理信号信息,因而有利于提取感兴趣的信号成分。小波相干和相位相干分析刻画了两个随机过程在时频域上的同步关系,将它们应用于分析驾驶员的心脑功能连接特性可以为驾驶负荷的评价提供一个新的视角。
[Abstract]:In recent years, the number of cars has increased year by year. Literature studies show that drivers are prone to sudden stroke and other cardiovascular and cerebrovascular diseases in the course of driving, among which high load driving is an important inducing factor. In order to meet the multifunctional requirements of the automobile, the man-machine interface of the vehicle is becoming more and more complex, which undoubtedly increases the driver's driving load. It has become an important subject in the field of automotive ergonomics to evaluate the human-computer interface design of the cab scientifically. Therefore, the purpose of this paper is to analyze the driving load from the angle of the driver's mental and brain function connection, and to provide a reliable theoretical basis for the optimization design of the man-machine interface. The state of cardio-cerebral connection can reflect the ability of brain to regulate cerebral blood flow in the face of changes in blood pressure. In response to high driving load, brain oxygen consumption is bound to increase. Adequate cerebral blood perfusion is required to ensure normal metabolic activity of brain cells. Therefore, it is feasible to evaluate the driving load by studying the dynamic adjustment ability of the driver's heart and brain function. Near-infrared spectroscopy (NIR) has been recognized as a very suitable technique for the analysis of cerebral blood flow regulation. On the one hand, it has not only good temporal resolution (close to milliseconds), but also good spatial resolution (5-10 mm), which can detect changes in oxygenated hemoglobin concentration in the microcirculation of brain tissue, combined with arterial blood pressure. It is possible to localize the assessment of the brain's cardiac and brain function connections. On the other hand, the oxygen metabolism signal based on the near infrared spectrum is not easily disturbed by the moving noise, and the corresponding detection equipment is portable and can be used to detect the driver in real time. Firstly, this paper simulates the driving load of the driver in the actual driving process by designing the alert operation task platform. Then the cerebral blood oxygen signals in the prefrontal and motor regions were collected by using near-infrared spectrometer, and the arterial blood pressure signals of the drivers were collected by using the blood pressure meter. Finally, based on the coherence method of Morlet, wave, the coupling relationship between the two signals in the characteristic low frequency band is analyzed to evaluate the influence of driving load on the functional connection of the driver's heart and brain. It is found that the wavelet coherence and phase coherence are significantly different from those in the rest state in some frequency bands under the alert operation task state. The results showed that driving load had a significant effect on cardiac and brain functional connections. Through the research in this paper, it can be concluded that the method based on wavelet change can display more physiological signal information from the time-frequency plane, so it is advantageous to extract the interested signal components. Wavelet coherence and phase coherence analysis describe the synchronization relationship between two stochastic processes in time-frequency domain. Applying them to the analysis of drivers' mental and brain functional connections can provide a new perspective for the evaluation of driving load.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：U491.254

【相似文献】