三維手部動作的分析與量測對手部疾病之診斷與治療能提供很大的幫助。在臨床上，醫師以專業經驗指定手部動作與進行靜態的手指角度量測；至於手部動態運動分析的研究，目前通常利用受試者在指定動作下之螢光攝影影像進行參數分析，而缺乏整體動態的三維模型以提供參考比對。本論文之主要目標是以研究如何建立患者手指部位之三維模型以及量測其連續運動；經由低成本攝影機之連續動態影像，利用電腦視覺技術建立三維虛擬手指模型，發展一套整合之三維手指動作分析與量測系統，取代昂貴的運動分析設備與提供可靠的手部動態資訊以協助外科醫師對患者病灶區域進行臨床診斷、治療規劃與術後評估。

　我們利用電腦視覺的三維資訊重構技術與相機校準技術來計算標記點在空間中之三維資訊，然後依據手部運動學模型去計算相關指間角度值與手指長度值之參數，同時利用基本的立體幾何模型建構虛擬手掌三維動態模型。擁有上述之靜態與動態虛擬手掌模型後，我們結合影像資訊與三維標記點位置，採用創新的Marker-guided particle filter演算法實現手部運動的動態追蹤，並可以即時計算出所有的最佳手部參數值。為了提升模型的精確性，本論文最後會將MRI所得到的三維手骨實際放入所建構之虛擬指塊模型中；如此，我們可以交替驗證手傷患者在動作時之骨骼之動作狀況，量測出動態運動參數或評估相關復健情況，同時對手指病變也可以用定性與定量方式做出客觀分析與診斷。

　由實驗顯示，本論文所提的系統在運算速度和精準度上都有不錯的結果，可對表皮標記點資訊做補償，這是目前使用的運動分析系統無法做到的。而且本方法僅需市售的數位相機，與醫院昂貴的運動分析系統相比，成本低廉且架設簡單，非常適合讓醫生對手指病變作一個客觀的分析和評估。

　3D motion analysis and measurement provides valuable information to medical doctors for diagnosis and treatment of hand diseases. However, at present, researchers usually use the fluoroscope images taken from tester under several specific postures to perform hand motion analysis, which lacks of a global dynamic model for comparison and is not a real 3D motion analysis. The main goal of this research focuses on constructing the real 3D human hand model and measuring its successive motion from video. We utilize the computer vision techniques to build 3D virtual hand model with image sequences from a video camera and control an integrated 3D hand analysis and measurement system, which will help surgeons for clinical diagnosis, therapy planning, and post-surgery evaluation.

　In the proposed system, markers are placed on joint regions of tester’s fingers, and then videos of finger motion under given gestures are captured. After obtaining the data, we calculate the 3D information of the markers, and utilize image available information to compute the angle and length parameters according to the hand kinematical model. The 3D dynamic virtual hand model is represented by the components of cylinder and sphere. With dynamic virtual hand model, we can measure the dynamic motion parameters, evaluate the recovery progress, and perform analysis and diagnosis qualitatively and quantitatively for hand diseases.

　Experimental results show that the proposed method reduces the computation cost, and keeps the accuracy of estimated parameters still satisfactory. Although the conditional motion analysis system is precisely calibrated, it only provides the information of tracked markers which can hardly reveal the true kinematics of human fingers. Besides, it can not capture finger motion with fluoroscopy simultaneously due to technical limitation. In addition to marker prior information, our system integrates image and shape information to rectify model parameters, and show its great portability to work with fluoroscopy. Our system requires only two common digital cameras that makes it a low-cost solution for finger diseases diagnosis and finger kinematics evaluation.

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