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研究生: 蔡昇嘉
Tsai, Sheng Chia
論文名稱: 運用多尺度熵與迭代高斯濾波器於人體平衡狀態分析之研究
Analysis of Postural Sway Dynamics using Multi Scale Entropy and Iterative Gaussian Filter
指導教授: 吳順德
Wu, Shuen-De
學位類別: 碩士
Master
系所名稱: 機電工程學系
Department of Mechatronic Engineering
論文出版年: 2012
畢業學年度: 100
語文別: 中文
論文頁數: 79
中文關鍵詞: 加速規迭代高斯濾波器多尺度熵
英文關鍵詞: Accelerometer, Iterative Gaussian filter, Multi Scale Entropy
論文種類: 學術論文
相關次數: 點閱:201下載:0
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  • 近年來在醫學領域與大腦研究方面,有相當多關於人體平衡分析的文獻,也發展出各種的不同的技術與方法,但主要的量測方法取決於檢測儀器與感測器,像是測力板或加速規。所以在本文中提出一個有效的分析方法,可從人體晃動的狀況來分析不同平衡狀態的差異。分析的方法主要包含三個步驟:首先會透過三軸加速規來量測人體晃動的加速度訊號,接著再使用迭代高斯濾波器來濾除訊號的雜訊部分,此濾波器不會有相位誤差且較能分離出相異的訊號。之後再使用多尺度熵來量化濾波後的訊號,此方法較常用於訊號複雜度的分析。從實驗數據來看,我們認為多尺度熵的曲線可比較出各種不同狀態的差異,當人體處於較不平衡的狀況時,多尺度熵的曲線也會隨著降低。在實驗設計方面則分為兩個部分,首先是找出視覺對人體平衡的影響,例如張開雙眼與閉上雙眼;接著是針對人體在大腦思考時是否會對平衡造成影響,例如數學運算與英文閱讀。最後由實驗結果可獲得兩點結論:(1)當人體喪失視覺的時候會使平衡感降低;(2)人體思考的時候會占用大腦的資源,進而降低其平衡能力。另外由這些實驗的結果顯示本文所提出的分析方法,在人體平衡量測方面的效果相當顯著也具有可行性。

    The analysis of human equilibrium, also known as postural stability, is a topic of great interest for the brain research and medicine community. A wide range of techniques and methodologies has been developed, but the choice of instrumentations and sensors depends on the requirement of the specific application. In this paper, we propose a methodology to analyze the human sway dynamics under several different stability conditions. The proposed methodology consists of three major steps: Firstly, the human postural sway acceleration signals were collected by using some 3-axis MEMS accelerometers. Secondly, we use a zero phase filter, named Iterative Gaussian Filter (IGF), to remove the noise from the collected signals. Thirdly, a popular complexity measure, named multi-scale entropy (MSE), is used to quantify the complexity of the filtered acceleration signal. We found that the MSE curves can be used to quantify different human stability conditions. The MSE curves derived from weak-stability-conditions lies below that derived from strong-stability conditions. Several experiments were designed to study the effect of the vision (eyes closed, eyes open) and brain resources (calculation, reading) on the human postural stability. Through the experimental results, we give two hypotheses: (1) loss of vision will decrease the human postural stability; (2) decrease of the brain resources will decrease the human postural stability. These experimental results demonstrated the feasibility and effectiveness of the proposed methodology on the analysis of human postural sway dynamics.

    摘要 i 英文摘要 ii 誌謝 iii 目錄 iv 圖目錄 vi 表目錄 viii 第一章 緒論 1 1.1 前言 1 1.2 研究動機與目的 2 1.3 論文架構 3 第二章 相關研究與系統架構 4 2.1 平衡相關文獻 5 2.2 加速規基本原理 7 2.3 ADXL345開發板架構 9 2.3.1 微處理器(Atmega328, MCU) 11 2.3.2 加速規(Accelerometer) 13 2.3.3 記憶卡(Secure Digital Card) 15 2.3.4 串列匯流排介面(Serial Peripheral Interface,SPI) 17 2.4 加速規暫存器程式設定 19 2.4.1 加速規暫存器功能 20 2.4.2 加速規傳輸速率設定 22 2.4.3 加速規資料格式 22 2.4.4 加速規程式設計與燒錄 23 2.5 數據資料處理 25 2.5.1 數據格式轉換 25 2.5.2 加速規校準 26 第三章 訊號分析與處理方式 29 3.1 傾角轉換方式 30 3.1.1 雙軸角度轉換 30 3.1.2 三軸角度轉換 32 3.2 訊號向量強度(Signal Vector Magnitude, SVM) 34 3.3 頻譜分析(Fast Fourier Transform, FFT) 37 3.4 迭代高斯濾波器(Iterative Gaussian Filter, IGF) 38 3.5 多尺度熵(Multi Scale Entropy, MSE) 42 第四章 實驗流程與結果 45 4.1 一般性平衡實驗設計 45 4.2 一般性平衡實驗結果 47 4.2.1 原始訊號的MSE與角度轉換後的MSE比較 47 4.2.2 原始訊號取SVM前後的MSE比較以及濾波前後的差異 48 4.2.3 原始訊號取SVM值經過濾波的MSE比較 50 4.3 視覺與大腦思考實驗設計 55 4.3.1 數學運算實驗 57 4.3.2 英文閱讀實驗 58 4.3.3 3D圖形實驗 59 4.4 視覺與大腦思考實驗結果 60 4.4.1 數學運算實驗 60 4.4.2 英文閱讀實驗 64 4.4.3 3D圖形實驗 67 4.5 MSE統整分析與比較 70 第五章 結論與未來展望 73 參考文獻 75 附錄A-實驗資料 79

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