本論文主旨在於使用慣性感測器中之運動訊號將其轉換成身體質量中心晃動程度分級之時空間特徵於平衡能力做評估，並將自適應增強分類演算法應用於阿茲海默氏症之分類。首先，本論文設計了動、靜態平衡實驗來評估受測者之平衡能力。本論文請會請受測者穿戴上慣性感測器裝置於腰部以及腳尖上，用以偵測平衡實驗時所收到之運動訊號。並將取得之訊號做前處理及校正後使用來做分析及評估。再接著將腰部慣性感測器之訊號轉換成身體質量中心之軌跡，並將其軌跡透過身體質量中心搖晃分級之方式來分析受測者之搖晃之資訊，再接著將腳部慣性感測器所取得之運動訊號取得其特徵，將腳部以及腰部慣性感測器訊號之特徵應用於自適應增強分類演算法做分類。最後，我們期許此論文中提出的身體質量中心晃動程度分級可以幫助醫生在平衡分析上多一些評估的依據，也希望此分類演算法可以協助醫生在診斷上更便利。

The purpose of the thesis is use the sport signal of inertial sensors to calculated the spatiotemporal patterns of the body center of mass sway level for determine subjects’ balance ability. Then, use the adaboost algorithm for Alzheimer’s disease subjects to classification. First, the thesis designed a series of dynamic and static balance procedures to determine subjects’ balance ability. The thesis will asked the subjects wore the inertial sensors on waist and toe to obtain the sport signal in the balance procedures. And then, we will use the signal preprocessing and calibration to the inertial sensor signal for analysis and estimation. Next, the signal of the waist inertial sensor will be calculated to the trajectory of the body center of mass, then use the trajectory and body center of mass sway level to analyzed subjects’ information of sway level. And next, the sport signal of the foot inertial sensor will be calculated the sport features. We applied the adaboost algorithm with the foot inertial sensor signal features and the waist inertial sensor signal features for classification. Finally, we hoped the body center of mass sway level in the thesis could help the medical personnel to got more information to evaluated the balance ability for Alzheimer’s disease.

摘要 I
SUMMARY II
Chapter1 介紹 1
1-1 動機 1
1-2 文獻探討 1
1-3 論文目的 3
1-4 論文架構 4
Chapter2 實驗設計 5
2-1 神經心理學測驗 5
2-1-1 MMSE 5
2-1-2 CASI 6
2-2 實驗裝置 6
2-3 平衡實驗 7
2-3-1 動態平衡實驗 7
2-3-2 靜態平衡實驗 9
Chapter3 身體質量中心搖晃程度分級及平衡能力 11
3-1 訊號前處理 12
3-1-1 校正 12
3-1-2 低通濾波 14
3-2 動態平衡之時間特徵擷取 14
3-3 身體質量中心晃動與平衡能力 17
3-3-1 身體質量中心在AP/ML平面上之投影 17
3-3-2 身體質量中心晃動程度分級之定義 21
Chapter4 阿茲海默氏症分類演算法 26
4-1 動態及靜態平衡實驗之特徵擷取 26
4-2 特徵正規化 30
4-3 自身適應增強分類演算法Adaptive boosting(Adaboost) 30
Chapter5 實驗結果與討論 33
5-1 受測者 33
5-2 動態平衡實驗 35
5-3 靜態平衡實驗 35
5-4 Adaboot分類結果 37
5-4-1 動態平衡分類結果 37
5-4-2 靜態平衡分類結果 40
5-5 討論 42
Chapter6 結論及未來工作 43
6-1 結論 43
6-2 未來工作 43
Reference 45

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