臺灣博碩士論文加值系統

近年來，隨著人們節能減碳和健康樂活理念的提升，自行車的使用率節節攀升。自行車具有交通運輸、休閒以及運動健身等功能，更廣泛應用於下肢傷害的復健，然而不正確的使用也會導致關節肌肉的損耗。為了正確引導自行車使用者的踩踏姿勢以及指導自行車把手及座椅的設計與調整，有必要對腳踏車踩踏做生物力學分析。
本研究利用單個深度攝像頭（KINECT v2），加入全域最優化演算法（GOM）以及卡爾曼濾波（Kalman filter）開發精準可靠的三維動作捕捉系統，配合力學量測設備，建立個人化之下肢生物力學模型來求得踩踏自行車時下肢關節之生物力學變數，並開發一完整易於使用之有用戶介面的動作分析軟體。系統開發完成後利用Vicon紅外立體攝影系統作為標準，以自行車踩踏動作來作為驗證動作，評估本研究開發之系統之可靠性和精準度。
本研究順利完成系統之開發,並利用多圈軌跡疊加與擬合的方法可以在75RPM以內的轉速之下取得與Vicon相接近的關節角度的準確度。

In recent years, with the improvement of people''s concept of energy-saving, carbon reduction, and health and well-being, the utilization rate of bicycles has been rising. The bicycle has the functions of transportation, leisure, sports, and fitness, which are also widely used in the rehabilitation of lower limb injuries. However, improper use can also lead to wear down of joints and muscles. In order to correctly guide the pedaling posture of bicycle users and guide the design and adjustment of bicycle handlebar and seat, it is necessary to do biomechanical analysis on pedaling.
In this study, the depth camera (KINECT v2) was used, the global optimization method (GOM) and Kalman filter were added to develop an accurate and reliable 3d motion capture system, and the personalized biomechanical model of the lower limb was established in conjunction with the mechanical measurement equipment to obtain the biomechanical variables of the lower limb joints during pedaling.
This study successfully completed the development of the system, and the accuracy of the joint Angle close to Vicon can be obtained by using the method of multi-loop trajectory superposition and fitting within the rotation speed of 75RPM.

目錄
誌謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 VII
第一章 緒論 1
第一節 研究背景 1
第二節 動作分析的介紹及其應用 2
第三節 深度攝影機之簡介 4
第四節 臨床動作分析應用上的瓶頸 10
第五節 發展目的 10
第二章 動作分析之理論基礎及定義 11
第一節 基本假設 11
第二節 自行車踩踏週期定義 11
第三節 關節中心位置之定義 12
第四節 下肢局部坐標系統之定義 12
一、 坐標系統之簡介 12
二、 關節坐標系統 13
三、 下肢模型定義方式 14
第五節 GOM之方法介紹 15
第六節 卡爾曼濾波原理介紹 17
第三章 系統開發 19
第一節 開發環境 19
一、 系統目標 20
二、 系統架構圖 20
第三節 系統功能 21
第四節 資料流程 22
第四章 系統驗證實驗 24
第一節 受試者 24
第二節 實驗設備 25
第三節 實驗流程 26
第五章 結果 29
第一節 程式功能及使用簡介 29
第二節 靜態校準準確性分析結果 32
第三節 GOM演算法最佳化結果 33
第四節 動態關節角度分析結果 33
第五節 軌跡疊加和擬合結果 38
第六節 軌跡轉換至時域的結果 38
第七節 軌跡疊加後的關節角度結果 39
第六章 討論 40
第一節 程式功能討論 40
第二節 靜態校準準確性討論 40
第三節 GOM演算法最佳化討論 41
第四節 動態關節角度討論 41
第五節 軌跡疊加與擬合結果討論 41
第六節 軌跡轉換至時域的結果討論 42
第八節 軌跡疊加後的關節角度結果討論 42
第七章 總結 43
第一節 結論 43
第二節 研究限制 43
第三節 未來發展 43
參考文獻 44

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