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研究生: 簡彥文
論文名稱: 基於嵌入式系統之於人體步態分析
Gait Analysis of Human Body on embedded system
指導教授: 邱瀞德
口試委員: 黃元豪
范倫達
學位類別: 碩士
Master
系所名稱: 電機資訊學院 - 通訊工程研究所
Communications Engineering
論文出版年: 2014
畢業學年度: 102
語文別: 英文
論文頁數: 53
中文關鍵詞: 步態分析重心慢性阻塞性肺病(COPD)
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  • 步態分析在很多應用中都扮演者一個重要的腳色,像是在病理症狀,身體狀況,醫療分析和運動學等等。從步態分析裡我們得知步伐速度、步伐距離、步伐時間和腳的移動軌跡和角度進而加以觀察一個人的生理資訊,像是腳在受傷前和受傷後的活動角度變化,擁有慢性阻塞性肺病(COPD)的人在步態測試中會有步伐趨緩的情形或者一些運動選手需要記錄自己的運動情形。
    在這篇論文裡,我們提出了利用攝影機抓取人體並使用重心和重心到人的輪廓所建立的距離圖進而建立人體的模型包括頭、軀幹上部、軀幹下部、手和腳,並取這七個部分各自的重心當作我們的特徵點。在行走行為中,利用這些特徵點我們可以得到身體部位的角度資訊和步伐資訊去做步態分析,而得知測試者的身體狀況是否有異常。最後我們把演算法放到嵌入式系統搭配攝影機進行步態分析,如同在電腦上處理擁有良好的結果。


    The gait analysis always plays an important role in different kinds of applications such as the disease symptoms, health condition, medical analysis, kinesiology and so forth. In the gait analysis, we are able to obtain the information of the pace speed, pace distance, pace period, and the movement track and angle of one's feet in order to observe the physiology of a person. For example, we can make good use of the pace analysis to observe the angle variation of patients' feet before and after the injury, keep pace with situations of slower paces towards those with COPD disease, or record the exercise scenarios of some athletes.

    In this paper, we use video cameras to extract the human body outline and take advantage of the outline distance map measured between the gravity center and human body outline to establish the human body model including the head, upper body, lower body, hands and foot. What's more, we take the gravity center of these 7 different parts as our interest points. During the walking behaviour, we can obtain the angle and pace information of body parts to do the pace analysis by using these interest points and know if there is any abnormality for the health condition of subjects. In the end, we do the pace analysis with our proposed algorithm using cameras in the embedded system and receive good experimental results as shown on the C environment.

    1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Description . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Goal and Contribution . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2 Related Works 5 2.1 Gait Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6 3 Gait Analysis of Human Body 8 3.1 Background Subtraction . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Morphological Processing . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Connected Component Labeling . . . . . . . . . . . . . . . . . . . . 12 3.4 Find COGs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.5 Body Segmentation . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.6 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . 17 3.6.1 Angle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.6.2 Pace . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 4 Design and Implementation on Embedded System 23 4.1 Modi_ed Pre-Processing . . . . . . . . . . . . . . . . . . . . . . . . 23 4.1.1 Radar method . . . . . . . . . . . . . . . . . . . . . . . . . . 24 4.1.2 Kalman Filter method . . . . . . . . . . . . . . . . . . . . . 25 4.1.3 White Pixel Boundary method . . . . . . . . . . . . . . . . 26 4.2 Hardware Design of Pre-Processing . . . . . . . . . . . . . . . . . . 27 4.2.1 Erosion and Dilation . . . . . . . . . . . . . . . . . . . . . . 27 4.2.2 Connected Component Labeling . . . . . . . . . . . . . . . . 30 5 Experimental Results 32 5.1 Gait analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 5.1.1 The Characteristics of Angles when Walking . . . . . . . . . 32 5.1.2 The Characteristics of COGs when Walking . . . . . . . . . 35 5.1.3 The Characteristics of Pace Rhythm when Walking . . . . . 36 5.1.4 The Example Case of Knee Injury Patient . . . . . . . . . . 36 5.1.5 The Example Case of Patients with COPD disease . . . . . 38 5.2 The results of Radar method and Kalman Filter method . . . . . . 38 5.3 Implementation on Embedded System . . . . . . . . . . . . . . . . 39 5.4 Experimental results of Hardware Design . . . . . . . . . . . . . . . 43 5.4.1 Performance Evaluation of Erosion and Dilation . . . . . . . 43 5.4.2 Performance Evaluation of Connected Component Labeling . 44 6 Conclusions 47

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