本文選題：多誘因非合適駕駛狀態(tài) + 前額葉腦血氧飽和度　； 參考：《中南大學(xué)》2013年博士論文

【摘要】：摘要：隨著人類社會(huì)的老齡化及文化發(fā)展的多元化,交通事故的主觀誘因日趨復(fù)雜,這些誘因不僅包含傳統(tǒng)意義下的疲勞及分神行為,同時(shí)包括飲酒、吸毒、疾病、藥物等新型誘因。這些誘因致使駕駛主體進(jìn)入多誘因非合適駕駛狀態(tài)(Various Inducement Unfitted Driving States,VIUDS),降低了對駕駛環(huán)境的認(rèn)知能力,并最終導(dǎo)致交通事故。目前,傳統(tǒng)的方法只能實(shí)現(xiàn)單一誘因的檢測,而無法在統(tǒng)一的技術(shù)構(gòu)架下實(shí)現(xiàn)多種誘因的同時(shí)檢測。針對這一問題,本文以生物醫(yī)學(xué)及認(rèn)知科學(xué)的最新研究成果為基礎(chǔ),在分析了多種主觀誘因?qū)︸{駛主體中樞神經(jīng)系統(tǒng)認(rèn)知能力負(fù)面共性影響的基礎(chǔ)上,選取了面部表情及腦前額葉血氧飽和度作為基本特征量,通過貝葉斯網(wǎng)絡(luò)實(shí)現(xiàn)了認(rèn)知能力的融合評估,并最終在同一技術(shù)構(gòu)架下實(shí)現(xiàn)了VIUDS多誘因的統(tǒng)一檢測。本文的主要?jiǎng)?chuàng)新如下： 1、在分析交通事故多種主觀成因的基礎(chǔ)上,得出了在多誘因作用下VIUDS本質(zhì)上是中樞神經(jīng)系統(tǒng)認(rèn)知能力下降的狀態(tài)；在分析基于單特征量及現(xiàn)有檢測方法局限性的基礎(chǔ)上,選取了面部表情及腦血氧飽和度作為反映認(rèn)知能力的基礎(chǔ)特征量,為認(rèn)知能力的融合評估奠定了基礎(chǔ)； 2、提出了一種認(rèn)知能力的評估方法,該方法首先采用去趨勢波動(dòng)分析的方法計(jì)算腦額葉血氧飽和度時(shí)間序列的標(biāo)度指數(shù)(Cerebral Oxygenation Saturation Scaling Exponents,COSSE),并通過該指數(shù)實(shí)現(xiàn)了主體中樞神經(jīng)系統(tǒng)認(rèn)知能力的分階評估； 3、提出了一種基于石英音叉及橢球面聲聚焦光聲傳感結(jié)構(gòu)的腦額葉血氧飽和度檢測方法。在檢測過程中,額葉組織及石英音叉分別位于橢球面的兩個(gè)焦點(diǎn),在光激勵(lì)脈沖的刺激下,焦點(diǎn)處額葉組織產(chǎn)生的弱光聲信號經(jīng)橢球面聚焦后被另一焦點(diǎn)處的石英音叉以共振的方式拾取,最后根據(jù)激勵(lì)光強(qiáng)、光聲信號強(qiáng)度及血氧飽和度之間的定量關(guān)系,可實(shí)現(xiàn)前額葉腦血氧飽和度的遠(yuǎn)場無創(chuàng)測量； 4、提出了一種正交流形保持投影的表情方法。針對流形保局投影方法的局限性,即不能在低維投影空間保持流形全局結(jié)構(gòu)的不變性及投影矩陣非正交的問題,重新定義了目標(biāo)約束函數(shù),并采用格拉姆-施密特正交化過程重新求解投影矩陣,不僅在投影空間有效地保持了流形的全局及局部結(jié)構(gòu),同時(shí)有效地降低了特征維數(shù),提高了表情識別的準(zhǔn)確率； 5、提出了一種基于貝葉斯網(wǎng)絡(luò)的認(rèn)知能力多特征融合評估方法,該方法采用腦額葉血氧飽和度時(shí)間序列的標(biāo)度指數(shù)、疲勞表情、眨眼的頻率等作為融合的基本特征,通過貝葉斯網(wǎng)絡(luò)計(jì)算了這些獨(dú)立隨機(jī)量對應(yīng)的后驗(yàn)概率,并采用后驗(yàn)概率作為認(rèn)知能力的評估依據(jù),從而有效地克服了采用單特征進(jìn)行檢測的局限性,提高了評估的精度。 論文在最后一章對全文工作進(jìn)行了總結(jié),并指出了后繼的工作及研究方向。本文包括圖33幅,表10個(gè),參考文獻(xiàn)159篇。
[Abstract]:Abstract: with the aging of human society and the diversification of culture, the subjective causes of traffic accidents are becoming more and more complex. These inducements not only include fatigue and distraction in the traditional sense, but also include drinking, drug taking and disease. New inducements such as drugs. These inducements cause the driving subject to enter the multi-inductive unsuitable driving state (VIUDS), which reduces the cognitive ability of driving environment and eventually leads to traffic accident. At present, the traditional method can only realize the detection of single inducement, but can not realize the simultaneous detection of multiple inducements under the unified technical framework. Aiming at this problem, based on the latest research results of biomedicine and cognitive science, this paper analyzes the negative common effects of various subjective inducements on the cognitive ability of central nervous system of driving subjects. The facial expression and prefrontal blood oxygen saturation were selected as the basic characteristics, and the fusion evaluation of cognitive ability was realized by Bayesian network, and finally the unified detection of multiple inducements of VIUDS was realized under the same technical framework. The main innovations of this paper are as follows: 1. On the basis of analyzing the subjective causes of traffic accidents, it is concluded that under the action of multiple inducements, VIUDS is essentially a state in which the cognitive ability of the central nervous system decreases; Based on the analysis of single feature quantity and the limitation of existing detection methods, facial expression and cerebral blood oxygen saturation are selected as the basic feature quantities to reflect cognitive ability, which lays a foundation for the fusion evaluation of cognitive ability. 2. A method for evaluating cognitive ability is proposed. In this method, the scalar index (Cerebral Oxygenation Satation scaling scale COSSE) of the frontal lobe blood oxygen saturation time series was calculated by the method of detrend fluctuation analysis, and the cognitive ability of the central nervous system was evaluated by the index. 3. A method based on quartz tuning fork and ellipsoidal acoustic focusing photoacoustic sensor structure is proposed to detect the cerebral frontal lobe blood oxygen saturation. During the detection process, the frontal lobe tissue and the quartz tuning fork were located in the two focal points of the ellipsoid, respectively, stimulated by the light excitation pulse. The weak photoacoustic signal produced by the frontal lobe tissue at the focal point is picked up by the quartz tuning fork at the other focal point by the ellipsoidal focusing surface. Finally, according to the quantitative relationship among the excitation light intensity, the intensity of the photoacoustic signal and the saturation of blood oxygen, The far field non-invasive measurement of cerebral blood oxygen saturation in prefrontal lobe can be realized. (4) A positive AC shape preserving projection expression method is proposed. In view of the limitation of the local projection method, that is, the invariance of the global structure of the manifold and the nonorthogonality of the projection matrix in the low-dimensional projection space, the objective constraint function is redefined. Using Gram Schmidt orthogonalization process to solve the projection matrix, not only the global and local structure of the manifold is effectively maintained in the projection space, but also the feature dimension is reduced effectively, and the accuracy of facial expression recognition is improved. 5. A Bayesian network based multi-feature fusion evaluation method for cognitive ability is proposed. The basic features of the fusion are the scalar index of the time series of cerebral frontal oxygen saturation, the expression of fatigue and the frequency of blink. The posteriori probability of these independent random variables is calculated by Bayesian network, and the posteriori probability is used as the basis for the evaluation of cognitive ability. Thus, the limitation of single feature detection is effectively overcome and the accuracy of evaluation is improved. In the last chapter, the thesis summarizes the work of this paper, and points out the following work and research direction. This paper includes 33 figures, 10 tables and 159 references.
【學(xué)位授予單位】：中南大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2013
【分類號】：U491.254;U492.8

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