本文選題：手寫運動　切入點：書寫困難　出處：《中國科學(xué)技術(shù)大學(xué)》2016年博士論文　論文類型：學(xué)位論文

【摘要】：手寫運動作為人體高級神經(jīng)系統(tǒng)控制的特有精細運動,與人體腦功能、認知水平具有密切相關(guān)性�，F(xiàn)有的手寫運動分析方法,存在研究技術(shù)手段單一、對健康群體關(guān)注度較少、手寫能力評價體系不全面等問題。本文重點針對身體各項機能處于快速發(fā)育的兒童,以及腦功能受到持續(xù)損傷的神經(jīng)退行性疾病患者這兩類群體,探討手寫運動在人體神經(jīng)系統(tǒng)發(fā)育及病變階段的表現(xiàn)特征,以理解手寫運動機能的動態(tài)變化特點。本文針對手寫運動發(fā)展、手寫運動損傷開展特征分析以及量化分類工作,主要研究內(nèi)容如下：(1)兒童手寫運動能力發(fā)展研究：手寫作為兒童進行思想表達、開展學(xué)業(yè)的一項基本方式,在兒童認知、心理發(fā)展過程中具有重要地位。對普通兒童的手寫運動發(fā)展規(guī)律進行研究,有助于建立兒童手寫運動常模數(shù)據(jù),并為不同年齡段病患兒童的手寫運動分析提供有效的對比數(shù)據(jù)源。本文基于手寫三維力信息分析了兒童手寫運動過程中的施力情況,并提出運動一致性特征參數(shù),結(jié)合運動學(xué)、動力學(xué)特征對學(xué)齡期兒童的手寫能力發(fā)展進行定量描述。結(jié)果表明高年級兒童具有更短的運動時間、更高的運動速度、速度曲線變化方向變化次數(shù)更少,更傾向于在書寫平面中心附近抄繪圖形、以施加更小的力及更小的能量完成手寫任務(wù)。所設(shè)計的非直接視覺反饋實驗方案,驗證了高年級兒童具有更強的空間位置感。提出的特征指標(biāo)在衡量手寫運動熟練度發(fā)展方面具備有效性。(2)手寫運動損傷特征分析：神經(jīng)退行性疾病會造成腦功能的持續(xù)損傷,藥物治療對病情有一定控制作用但仍然不能阻止病程的發(fā)展,探索新型有效的生物標(biāo)記,實現(xiàn)該類疾病的早期診斷以及病程跟蹤,具有重要意義。本文以神經(jīng)退行性疾病中具有代表性的帕金森病為對象,根據(jù)帕金森病的典型臨床癥狀并結(jié)合傳統(tǒng)量表的評價指標(biāo),設(shè)計了帕金森病患者手寫運動的定量檢測指標(biāo)。帕金森病患者在手寫運動患者在任務(wù)執(zhí)行時間、速度、加速度、分段寬度及面積、極徑曲線的方向變化次數(shù)NCR、極角頻譜峰值頻率PFA等指標(biāo)上表現(xiàn)出顯著差異,并驗證了手寫任務(wù)的設(shè)計對度量指標(biāo)的影響。表明手寫運動量化檢測方法,有助于發(fā)現(xiàn)潛在的人體精細運動功能異常變化情況,實現(xiàn)基于手寫運動的輔助診斷及病情發(fā)展跟蹤。(3)基于手寫運動特征和機器學(xué)習(xí)算法的輔助分類診斷：傳統(tǒng)的評分量表難以便捷量化地監(jiān)測病情進展,基于統(tǒng)計分析方法的單一性特征指標(biāo)分析也無法準(zhǔn)確地提供組間的有效區(qū)分,更具智能化的篩查檢測工具有待探索。本文基于手寫運動特征統(tǒng)計分析的結(jié)論,通過特征篩選和組合分類,構(gòu)建手寫運動特征集,引入機器學(xué)習(xí)方法實現(xiàn)群體分類,并分析了機器學(xué)習(xí)方法、手寫實驗任務(wù)、特征類型對分類效果的影響。利用手寫運動檢測結(jié)果進行手寫障礙的自動化分類篩查,提高分類識別的準(zhǔn)確度和有效性,有助于為相關(guān)疾病的診斷評估尋求新的技術(shù)手段提供思路。綜上所述,本文對不同群體提出了若干手寫運動評價檢測指標(biāo),實現(xiàn)對兒童手寫能力進化發(fā)育以及帕金森病患者手寫運動能力退化的評估,并構(gòu)建了手寫運動采集與分析系統(tǒng)。本工作將持續(xù)積累健康群體手寫運動樣本,促進基于計算機的量化評分模型與臨床應(yīng)用相結(jié)合,為相關(guān)病患的輔助診斷及藥效評估、手寫運動障礙干預(yù)訓(xùn)練提供支撐。
[Abstract]:Handwriting movement as a unique fine motor control of human advanced nervous system, and the function of human brain, cognitive level has close relationship. The analysis of existing methods of handwriting movement, study the existence of technical means of a single, less attention to health groups, writing ability evaluation system is not comprehensive. This paper focuses on the function of the body in the rapid development of children well, the brain function by neurodegenerative diseases in patients with sustained damage in these two groups, to explore the features of handwriting movement in human nervous system development and the stage of disease, in order to understand the dynamic change characteristics of hand movement function. According to the development of handwriting handwriting movement, sports injury characteristic analysis and carry out quantitative classification, the main research contents are as follows: (1) study on the development of sports ability of children: children are thought as handwriting handwritten expression, to carry out a study The basic method in children's cognitive, plays an important role in the process of the development of psychological research on handwriting movement. Development of ordinary children, contribute to the establishment of children's handwriting movement norm data, and for the different age of sick children handwriting movement analysis provides comparative data source effectively. This paper analyzes children's handwriting three-dimensional force information handwritten application of force in the process of movement and put forward based on coherence characteristics of motion parameters, combining kinematics, dynamics of the quantitative description of handwritten ability development of school-age children. The results show that the movement time of high grade children has shorter, higher speed, less speed change curve changes direction, more inclined to the writing center near the plane drawing copy, complete the task force and applied to handwritten smaller less energy. The non direct visual feedback experiment scheme, verification The spatial position of high grade children has a stronger sense. Characteristic index proposed effective measure in handwriting movement proficiency development. (2) analysis of sports injuries of handwriting: damage sustained neurodegenerative diseases can cause brain function, drug therapy has certain effects on disease control but still can prevent the development of disease. To explore new effective biomarkers, the early diagnosis of disease and disease tracking, which is of great significance. In this paper, neurodegenerative diseases typical of Parkinson's disease as the object, according to the typical clinical symptoms of Parkinson's disease and combined with the traditional evaluation index scale, designed for patients with Parkinson's disease handwritten quantitative index movement. The patients with Parkinson's disease in handwriting movement patients in the task execution time, speed, acceleration, segment width and area, change the direction of polar radius curve number NCR The polar angle, the peak of the frequency spectrum of PFA and other indicators showed significant differences, and verified the design of the writing tasks on metrics. That handwriting movement quantitative detection method, is helpful to find the abnormal changes of the fine motor function of the human body potential, realize the auxiliary diagnosis and condition based on handwritten motion track (3.) auxiliary classification diagnosis handwriting movement features and machine learning algorithm based on scale to convenient quantitative monitoring the disease progression in the traditional single feature index analysis method of statistical analysis can accurately distinguish between groups provided for based on the screening tool more intelligent to be explored. Based on the analysis of handwriting movement statistics based on the conclusion, through feature selection and combination classification, construction of handwriting movement feature set, machine learning method is introduced to realize the population classification and analysis of machine learning Method of handwritten experimental task, influence of feature types on the classification results. By using the motion detection results to classify handwritten handwriting disorder screening automation, improve the accuracy and validity of the classification is helpful to diagnose diseases evaluation for new technology and ideas. In conclusion, this paper puts forward some handwritten motion evaluation the detection index for different groups of children's development and evolution ability of handwritten handwriting assessment in patients with Parkinson's disease exercise capacity degradation, and construct a handwritten motion data acquisition and analysis system. This work will continue to accumulate health groups handwriting movement samples, promote quantitative scoring model combined with clinical application of computer aided diagnosis and efficacy is based on Assessment of patient, handwriting movement disorder intervention training to provide support.

【學(xué)位授予單位】：中國科學(xué)技術(shù)大學(xué)
【學(xué)位級別】：博士
【學(xué)位授予年份】：2016
【分類號】：R87;TP181

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