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系統識別號 U0026-1908201516013300
論文名稱(中文) 基於慣性感測器之三維步態軌跡重建系統應用於阿茲海默氏症之步伐高度分析
論文名稱(英文) 3D Gait Trajectory Reconstruction System Based on Inertial Sensor and Stride Height Analysis for Alzheimer’s Disease
校院名稱 成功大學
系所名稱(中) 電腦與通信工程研究所
系所名稱(英) Institute of Computer & Communication
學年度 103
學期 2
出版年 104
研究生(中文) 洪御庭
研究生(英文) Yu-Ting Hung
學號 Q36021046
學位類別 碩士
語文別 中文
論文頁數 57頁
口試委員 指導教授-詹寶珠
共同指導教授-白明奇
口試委員-鄭國順
中文關鍵字 阿茲海默氏症  慣性感測器  軌跡重建  步伐高度  支持向量機 
英文關鍵字 Alzheimer’s disease  Inertial sensor  Trajectory reconstruction  Stride height  Support vector machine 
學科別分類
中文摘要 高齡化社會的到來帶給社會很大的衝擊,阿茲海默氏症患者人數急遽上升,目前尚未有最佳的治療方法,因此早期判斷患病與否或者是後期追蹤病情便成為一個很重要的議題。
本論文設計了single-task與dual-task兩種步態實驗來分析受測者之步行能力。本論文會請受測者將慣性感測器裝置穿戴於右腳腳背上,接著請受測者進行步態實驗,慣性感測器裝置收集受測者之運動訊號。將收集到的訊號做前處理的動作後,便將訊號轉換成三維的步態軌跡。本論文也針對重建出之軌跡作驗證,使用相關性分析對動態分析系統之步態軌跡與本論文建立之軌跡做比較,並發現兩種軌跡之相關性為高度相關,由此可知本論文建立之軌跡並不遜色於市面上常使用之軌跡重建系統。
得到重建後之步態軌跡後可以取得步伐軌跡的步伐特徵包含步伐高度、步伐數及步伐長度,並利用此三種特徵配合支持向量機對正常人與阿茲海默氏症患者作分類,其分類出來之最佳準確率接近85%,最佳特異度接近83%。

最後,期許本論文開發之三維步態軌跡重建演算法應用於阿茲海默氏症之步伐高度分析能夠幫助醫師多ㄧ些早期評估或者是後期追蹤的依據,使醫師的診斷更全面。
英文摘要 The purpose of the thesis is use inertial sensors to collect the gait signal and reconstruct the participants’ gait trajectory. Then, we can classify the participants are Alzheimer disease patients or not. We designed a gait experiment including single-task and dual-task to analysis the participants’ dynamic balance ability. At first, participants put the inertial sensor devices on their right instep. Then, the inertial sensor devices collected the participants’ signal during the experiment. After signal preprocessing, we can use the signal to reconstruction three-dimension trajectory. After this, we should verify the trajectory’s rationality with the motion capture system. We use the correlation analysis to get the correlation coefficient between the inertial-sensor based trajectory and the motion capture system trajectory. We found that the two trajectory are highly correlated. After getting the trajectory, we can get the gait features including stride height, stride number, and stride length at the same time. Then, we use support vector machine-based classifier to separate Alzheimer’s disease patients and healthy participants. We can get the results after the classification. The optimal accuracy was closed to 85 percent, and the optimal specificity was closed to 83 percent.
論文目次 Chapter 1 介紹 1
1-1 動機 1
1-2 文獻探討 2
1-3 研究目的 3
1-4 論文架構 4
Chapter 2 基於慣性感測器之軌跡重建演算法 5
2-1 步態周期介紹 5
2-2 訊號前處理 7
2-2-1 校正 7
2-2-2 低通濾波器 10
2-3 步態偵測 10
2-4 三維軌跡重建 16
2-4-1 尤拉角 17
2-4-2 座標轉換 23
2-4-3 軌跡重建 25
2-4-4 步伐高度 27
Chapter 3 基於動態分析系統之軌跡重建 28
3-1 座標定位原理 29
3-2 反光標記球 29
3-3 建立軌跡 30
3-4 兩種軌跡之比較 31
Chapter 4 阿茲海默氏症之分類演算法 34
4-1 步態實驗之特徵擷取 34
4-2 特徵正規化 35
4-3 支持向量機分類演算法 35
Chapter 5 實驗設置與實驗結果 38
5-1 實驗設計 38
5-1-1 MMSE 38
5-1-2 CASI 39
5-1-3 實驗裝置 40
5-1-4 步態實驗 42
5-2 受測者 44
5-3 實驗結果 45
5-4 SVM分類結果 46
5-5 討論 47
Chapter 6 結論及未來工作 50
6-1 結論 50
6-2 未來工作 51
Reference 52
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