加速規在近年來由於微機電製程進步之下，持續的小型化及低價化，使得在工業、娛樂、醫療等領域上的應用更加廣泛，而將加速規應用在人體身上，能有助於我們取得更多傳統生理訊號以外的資訊，尤其是運動方面的狀態。
本研究的目標在於，利用加速規取得行走時的膝蓋加速度特徵，以達到生物認證的目標，相較於現今使用的各種方法中，利用加速規量測膝蓋加速度有資料能容易地大量收集，以及不易被盜取及模仿的優點。本論文中採用向量量化編碼及類神經網路分類器，進行三階段的分類，以及超球半徑分類的兩種不同方法。在個人化的應用下，三階段類神經網路分類法的測試結果不盡理想，有待修正的空間，而超球半徑分類法則有相當優異的特異度表現，平均都能有95%以上的特異度，而靈敏度也能隨設定能有彈性調整，最高可達85%以上，顯示以膝蓋加速度特徵作為生物認證可行性。

Abstract
With the rapid progress of the MEMs process, the cost and the size of accelerometers are reducing rapidly. As a result, accelerometers have found many new applications in industrial, entertainment and medical domains. One of such an applications is to acquire information about human body movement.
The objective of this work is to use knee acceleration signal for indentity verification. Comparing with traditional biometric methods, this approach has several distinct features. First, it can aquire a large amount of data efficiently and conventiently. Second, it is relatively difficult to duplicate. In designing the verification algorithm, this study has developed a neural network method a hyperspherical classifier method. The experimental results demonstrated that hyperspherical classifier provide better performances in this application. By setting the sensitively to 85%, the specificity achieved by the hyperspherical classifier is at least 95%.

目錄 I
圖目錄 IV
表目錄 VII
第一章 緒論 1
1.1前言 1
1.2研究動機與背景 2
第二章 資料截取及前處理 4
2.1硬體架構 4
2.1.1訊號擷取卡 5
2.1.2資料截取軟體架構 6
2.2前處理 6
2.2.1.平均值歸零： 7
2.2.2去除非穩定步行狀態部份 8
2.2.3能量正規化： 10
2.2.4頻譜內容分析 12
2.2.5離線低通濾波 14
2.2.6 重建低頻內容波型 15
2.2.7搜尋峰值位置 16
2.2.8步伐長度檢查 17
2.2.9步伐切割： 19
2.2.10步伐長度正規化 19
第三章 三階段類神經網路分類 20
3.1關心區域截取 20
3.1.2五筆取平均 22
3.2 VQ編碼簿產生 23
3.3 VQ編碼 25
3.4類神經網路訓練 26
3.5三段類神經網路分類 28
3.6實驗驗證 30
3.7測試結果 31
第四章 超球半徑分類法 33
4.1 前處理 35
4.1.1 關心區域截取及五筆取平均值 35
4.1.2減少資料維度 35
4.2 VQ編碼簿產生 36
4.3決定超球半徑 38
4.4實驗驗證 41
4.5實驗結果 43
第五章 結論與未來展望 45
參考文獻 47

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