【摘要】：隨著社會的發(fā)展,我國少年兒童升學壓力越來越大,學習時間越來越長,近視發(fā)病率也隨之越來越高。據專家分析表明長時間的不良書寫坐姿是導致少年兒童近視和生長發(fā)育不良主要原因之一。良好的學習坐姿與他們的生長發(fā)育息息相關,及時地糾正和保持正確的坐姿對孩子的健康成長非常重要。紙質練習簿無法實現對學生書寫坐姿的監(jiān)督,預防方法主要靠老師、家長提醒,身體狀況出現問題后通過長時間地佩戴耳掛式或脊椎式坐姿矯正器。文字書寫教學系統(tǒng)為實時監(jiān)督書寫坐姿提供了技術條件。不規(guī)范書寫坐姿檢測作為該系統(tǒng)中交互結構方面的重要研究分支,為用戶不良坐姿的自動提醒和矯正提供真實、可靠的坐姿數據,是其不可或缺的前置工作。目前,坐姿識別以單類特征為主,如基于用戶輪廓的幾何特征法給出一種有害人體姿勢報警方案,基于膚色特征的坐姿識別方法等。單類特征識別不規(guī)范書寫坐姿的主要缺點是識別率偏低,本文針對低齡用戶的一些獨特不規(guī)范書寫坐姿提出多類特征融合識別方法。論文的主要工作及成果如下:第一、對坐姿檢測過程中涉及到相關檢測識別算法進行了詳細地闡述,介紹了運動目標檢測、膚色特征提取、SURF特征提取及多分類算法等算法的適應情況和優(yōu)缺點,為本文后續(xù)工作提供理論支持。第二、從生理學角度分析了坐姿原理。針對少年兒童坐姿的特點,將用戶書寫坐姿分為正確、趴寫、含筆、托腮、歪頭、駝背等8種類別。通過對用戶坐姿分析,應用膚色在YCbCr空間聚集在一片固定區(qū)域且在CbCr平面上投影為一個近似橢圓的特性,提取各類坐姿在不同亮度下的膚色特征,以膚色特征描述手、臉的空間位置關系;依據不同閾值進行坐姿的SURF特征提取,以SURF特征分布特點來表征坐姿特征信息。第三、針對單類特征識別不規(guī)范書寫坐姿識別率偏低的現狀,提出多類特征融合的不規(guī)范書寫坐姿分類方法。經單類特征分類得到各類坐姿識別正確率,計算出同類坐姿異類特征的融合權值,即同類坐姿的異類特征權值之和為一,且與識別率成正比例關系。然后,同類坐姿異類特征加權融合,BP神經網絡訓練識別。第四、實現不規(guī)范坐姿的監(jiān)測方案�；谕愖水愵愄卣骷訖嗳诤纤惴�,設計了坐姿檢測仿真系統(tǒng),以檢驗算法的可行性,并對單類特征坐姿識別方法進行了對比實驗,以體現算法的優(yōu)越性。經實驗分析表明,該方法的不規(guī)范書寫坐姿識別率比單類特征法有明顯提高,可為文字書寫教學系統(tǒng)的不良坐姿的自動提醒和矯正提供真實、可靠的坐姿數據,能夠提高少年兒童的不良坐姿檢測識別率,具有更好地實用性。
[Abstract]:With the development of society, the pressure of children entering school is increasing, the study time is longer and the incidence of myopia is higher and higher. According to the analysis of experts, long-term poor writing and sitting posture is one of the main causes of childhood myopia and growth dysplasia. Good study of sitting posture is closely related to their growth and development. It is very important to correct and maintain correct sitting posture in time for the healthy growth of children. The paper exercise book can not realize the supervision of students' writing sitting posture. The preventive methods mainly rely on teachers and parents to remind them that after physical problems, they wear ear-hanging or spine-type sitting posture correction devices for a long time. Writing teaching system provides technical conditions for real-time supervision of sitting posture. As an important research branch in the interactive structure of the system, non-standard writing sitting posture detection provides real and reliable sitting posture data for the automatic reminder and correction of the user's bad sitting posture, and is an indispensable pre-work of the system. At present, sitting pose recognition is mainly based on one kind of features, such as geometric feature method based on user profile gives a harmful human pose alarm scheme, sitting pose recognition method based on skin color feature and so on. The main disadvantage of one-class feature recognition is that the recognition rate is low. This paper proposes a multi-class feature fusion recognition method for some unique non-standard writing sitting posture of young users. The main work and achievements of this paper are as follows: firstly, the related detection and recognition algorithms are described in detail in the process of sitting and pose detection, and the moving target detection and skin color feature extraction are introduced. The adaptation, advantages and disadvantages of SURF feature extraction and multi-classification algorithm provide theoretical support for the follow-up work in this paper. Secondly, the principle of sitting posture is analyzed from the point of view of physiology. According to the characteristics of children's sitting posture, the user's writing sitting posture is divided into 8 categories, such as correct writing, writing down, writing with pen, supporting gills, crooked head, hunchback and so on. By analyzing the user's sitting posture, the skin color is gathered in a fixed area in the YCbCr space and projected on the CbCr plane as an approximate ellipse. The skin color features of various sitting posture under different luminance are extracted, and the hand is described by the skin color feature. The spatial position relationship of face; The SURF features of sitting posture were extracted according to different thresholds, and the feature information of sitting posture was represented by the distribution of SURF features. Thirdly, aiming at the low recognition rate of non-standard writing sitting posture in one-class feature recognition, a multi-class feature fusion method is proposed to classify the unstandardized writing sitting posture. The correct rate of seat pose recognition is obtained by one-class feature classification, and the fusion weights of different features of the same sitting posture are calculated, that is, the sum of the weights of different features of the same sitting posture is one, and it has a positive proportional relationship with the recognition rate. Then, the weighted fusion of different features of the same sitting posture and BP neural network training recognition. Fourth, the implementation of the non-standard sitting posture monitoring scheme. Based on the weighted fusion algorithm of the same sitting posture features, the simulation system of sitting posture detection is designed to verify the feasibility of the algorithm, and a comparative experiment is carried out to show the superiority of the algorithm. Experimental analysis shows that the recognition rate of non-standard writing sitting posture by this method is obviously higher than that of single-class feature method, and it can provide true and reliable sitting posture data for automatic reminder and correction of bad sitting posture in Chinese writing teaching system. It can improve the recognition rate of children's bad sitting and pose detection, and has better practicability.
【學位授予單位】：湘潭大學
【學位級別】：碩士
【學位授予年份】：2016
【分類號】：TP391.41
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本文編號：2457071