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研究生:劉宜明
研究生(外文):Yi-MingLiu
論文名稱:動作時區辨識於加速度計之即時性應用
論文名稱(外文):Motion Period Identification for Real-Time Application with an Accelerometer
指導教授:莊哲男
指導教授(外文):Jer-Nan Juang
學位類別:碩士
校院名稱:國立成功大學
系所名稱:工程科學系碩博士班
學門:工程學門
學類:綜合工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:56
中文關鍵詞:慣性量測元件即時人機互動系統動作時區辨識積分誤差
外文關鍵詞:inertial measurement unitsreal-time user interface systemmotion period identificationintegration error
相關次數:
  • 被引用被引用:0
  • 點閱點閱:164
  • 評分評分:
  • 下載下載:11
  • 收藏至我的研究室書目清單書目收藏:1
近年來,因為以微機電技術製造的慣性量測元件有體積小、耗能低、方便攜帶等種種特點,所以許多人開始應用慣性量測元件在人機互動的介面。現今慣性量測元件的相關應用主要在動作辨識,例如:手寫辨識。這樣的辨識應用有動作時間短暫和不需要即時性的輸出等兩個特點。因此這樣的應用幾乎採取離線式的演算法。未來,慣性量測元件在人機互動的應用,也將漸漸進展到需要即時性運算和求出速度與位移的應用層面,因此本篇論文針對即時性人機互動系統進行研究與實驗。
準確地辨識出動作時區是應用慣性量測元件中很重要的一環,舊有的演算法將收到的資料先經過移動平均濾波器處理,把高頻雜訊濾掉後再進行動作時區辨識。但是在測試後,我們發現移動平均濾波器造成輸出結果有時間延遲的現象。在經過實驗與理論推導後,我們得知,只要可以對原始的資料進行準確的動作時區辨識,我們便可將移動平均濾波器給省略,進一步解決時間延遲的問題。
我們利用資料相關性的概念,提出了一個新的即時動作時區辨識演算法(motion period identification),這個方法解決了離線式的問題。除此之外,因為這個時區辨識演算法的三個門檻值差距非常大,所以這些門檻值的設定比起其他演算法更為容易與有彈性。實驗結果證明,這個即時演算法能達到百分之十以下的積分誤差率。論文的最後使用自回歸模型對動作建模,讓原本較不規則的結果,變成符合積分誤差累積現象的結果,這對將來做補償時有很大的幫助。

Inertial measurement units (IMUs) have been widely applied in the human-machine interference. Their main application is in motion recognition, e.g. handwriting recognition, whereas such short-time motion recognition is almost realized by off-line algorithms. It is crucial to accurately identify the motion period while using IMUs. The traditional algorithm is implemented with the received signals from IMUs whose high frequency noise is removed
by a moving average filter. After doing experiments and analysis, we found, that actual moving average filtering is not necessarily required.
In this thesis, we propose a new real-time algorithm of motion period identification that is able to characterize the Autocorrelation History (AH) of the received raw signals. This new algorithm has three AH thresholds for detecting the beginning, middle, ending of an action. In comparison with to other algorithms, these three thresholds can be set easily and flexibly since their ranges are fairly large and different. Our experimental results show that this new real-time algorithm can make the ratio of the integration error below 10%. Finally, we use an autoregressive (AR) model to model the action signals. This AR model can make the integration result match that of integration error accumulation, which is very helpful while doing error compensation.

中文摘要. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . i
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
誌謝. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Contents . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv
List of Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vii
List of Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .viii
1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 Research Background . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1.2 Motivation and Objective . . . . . . . . . . . . . . . . . . . . . . . 1
1.3 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.4 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2 System Structure and Experiment . . . . . . . . . . . . . . . . . . 5
2.1 System Flow Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
2.2 Introduction to Hardware . . . . . . . . . . . . . . . . . . . . . . . .5
2.2.1 Arduino Platform (MCU) . . . . . . . . . . . . . . . . . . . . . . . 5
2.2.2 3-Axis Accelerometer Hitachi H48C . . . . . . . . . . . . . . 7
2.2.3 Dual-Axis Gryo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9
2.2.4 Robot Motor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
2.3 Introduction to Experiment . . . . . . . . . . . . . . . . . . . . . .11
3 Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14
3.1 Previous Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .14
3.2 Data Windows . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .16
3.3 Signal Magnitude Area . . . . . . . . . . . . . . . . . . . . . . . . . 16
3.4 Numerical Integral Method . . . . . . . . . . . . . . . . . . . . . . 17
3.4.1 Trapezoidal Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
3.4.2 Simpson Rule . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
4 Real-Time Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.1 Method Flow Chart . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2 Accelerometer Compensate . . . . . . . . . . . . . . . . . . . . . 20
4.2.1 Scaling Factor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
4.2.2 Compensation Bias Value . . . . . . . . . . . . . . . . . . . . . .21
4.2.3 Compensation Gravity Value . . . . . . . . . . . . . . . . . . . 21
4.3 Moving average . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
4.4 New Method Flow Chart . . . . . . . . . . . . . . . . . . . . . . . . 24
4.5 Autocorrelation History Motion Period Identification . 25
4.5.1 Autocorrelation History (AH) . . . . . . . . . . . . . . . . . . . 26
4.5.2 State Transition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
4.6 Data Integration and Autoregressive Model . . . . . . . . 30
4.6.1 Autoregressive Model . . . . . . . . . . . . . . . . . . . . . . . . 31
5 Simulation Results and Analysis . . . . . . . . . . . . . . . . . . . 33
6 Conclusion and Future Work . . . . . . . . . . . . . . . . . . . . . . 51
6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .52
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
個人簡歷. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .56

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[15] E. Foxlin, ``Pedestrian tracking with shoe-mounted inertial sensors,' Computer Graphics and Applications, vol. 25, pp. 38-46, November - December 2005.
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