本論文利用微機電陀螺儀、加速度感測器與磁場感測器之整合電路模組及其感測器校正程序與姿態估測演算法，來加以實現低成本、輕重量、小體積及低耗能之即時動作捕捉系統。本動作捕捉系統包含硬體電路及韌體設計、感測器校正程序、姿態估測演算法與人機介面等部分。在硬體及韌體設計方面，微機電感測器整合電路模組由一個三軸陀螺儀、一個三軸加速度感測器、一個三軸磁場感測器、一個微控制器及一個藍牙無線模組所構成。使用者在每個肢節各配戴一個微機電感測器整合電路模組，由微處理器透過控制器區域網路(Controller Area Nettwork, CAN)將各部位感測器之資訊彙整並透過藍牙無線傳輸模組將感測訊號發送至個人電腦並進行感測器校正程序與姿態估測演算法來加以估測人體各部位之姿態。感測器校正程序用以修正感測器之尺度因子、偏移量與軸向非正交等誤差；而姿態估測演算法主要利用擴展式卡爾曼濾波器(Extended Kalman Filter, EKF)進行姿態估測，其中我們利用陀螺儀量測之角速度來建立狀態轉移模型，利用加速度感測器與磁場感測器量測之重力向量與磁場向量來建立觀測模型。本論文所開發之人體動作捕捉系統包含了以下優點：(1)可攜式且使用時不用場地限制、(2)此感測器校正程序可有效地降低感測器本身所存在的誤差及(3)此姿態估測演算法可準確地估測運動姿態。最後，透過人機介面即時顯示人體各部位之姿態利用與Vicon光學式動作捕捉系統之實驗結果比較後，可成功地驗證本人體動作捕捉系統及其感測器校正程序與姿態估測演算法之有效性。

This thesis realizes a low cost, small size, light weight, low power consumption, and real-time motion capture system. The proposed motion capture system includes hardware circuits, firmware design, sensor calibration procedure, attitude estimation algorithm, and human-machine interface. For the hardware circuits and firmware design, each integrated circuit module is composed of a triaxial gyroscope, a triaxial accelerometer, a triaxial magnetometer, a microcontroller, and a Bluetooth wireless transmission module. The proposed integrated circuit modules are deployed on a user whole body including all moveable parts to collect the signals of body movement generated by the accelerometer, gyroscope, and magnetometer in each integrated circuit module. The signals are collected by the microcontroller of each module and sent to a host module through a controller area network (CAN) bus. Finally, the collected signals are transmitted to a personal computer via the Bluetooth wireless module of the host. The sensor calibration procedure is used to reduce the scale factor error, offset, and axial non-orthogonal error. The attitude estimation algorithm utilizes the extended Kalman filter to estimate the attitude angle of each limb. The advantages of the motion capture system include the following: 1) It is portable and can be used anywhere without any external reference device or ambit limitations, 2) the sensor calibration can reduce the sensors’ intrinsic errors effectively, and 3) the attitude estimation algorithm can estimate body postures accurately. The body postures generated by movements can be displayed via the human-machine interface. The comparison results between the proposed system and the Vicon optical motion capture system have successfully validated the effectiveness of the proposed system and its associated sensor calibration procedure and attitude estimate algorithm.

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