臺灣博碩士論文加值系統

隨著微機電系統技術的演進，使得各式各樣的感測元件被快速地發展出來。感測元件被運用在環境監控、居家照護、工廠自動化及行動應用等方面。其中加速規感測元件可取得加速度資訊且已被廣泛應用在行動裝置中以開發各式應用軟體。以加速度資訊作為手勢動作識別的演算法已被提出來，相關研究包含主成分分析、類神經網路、支持向量機及動態時間校正等方法。而上述的演算法都需要經過測試樣本的訓練及校正。因此，本文提出一個加速度特徵值演算法來識別使用者的手勢動作。為解決行動裝置的運算限制，本文所提之手勢識別應用系統將使用智慧型手機、無線網路技術與模糊分類來達成直覺式操作的目的。實驗結果顯示本文所提之方法可判斷出簡單的動作，如向左傾斜、翻轉、搖晃和Z字形等動作，且具高之動作識別準確率。

With the evolution of Micro Electro Mechanical Systems (MEMS) technology which makes a variety of sensors to be quickly developed. The sensing element is used in environmental monitoring, home care, factory automation, and mobile applications. Accelerometer sensing element can be used to obtain acceleration information, and has been widely used in smart mobile devices to develop all kinds of application software. Some gesture recognition algorithms to deal with acceleration information have been proposed. Recently, gesture recognition techniques include Principal Component Analysis, Artificial Neural Network, Support Vector Machine and Dynamic Time Warping methods. These methods usually utilize testing samples for system training and refinement. This paper proposed an acceleration feature based algorithm to identify the user''s gestures. In order to reduce the computational limitations, the proposed gesture recognition system utilize sever to dead with the gesture recognition process via wireless network and to achieve the goal of the intuitive control via high gesture recognition accuracy. Experiment results show that the proposed method can identify for the left tilt, flip, shaking, and Z-shaped gesture action with high recognition accuracy.

中文摘要 I
Abstract II
誌謝 III
目錄 IV
表目錄 VII
圖目錄 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 4
第二章、 文獻探討 6
2.1 加速規感測元件介紹 6
2.2 加速規運用於手勢識別之相關研究 7
2.3 手勢識別運算相關研究 11
2.3.1 主成分分析 11
2.3.2 類神經網路 13
2.3.3 支持向量機 (Support Vector Machine, SVM) 15
2.3.4 動態時間校正 17
2.4 模糊理論 18
2.4.1 模糊集合 (Fuzzy Set) 19
2.4.2 模糊歸屬函數 20
2.4.3 模糊運算 23
2.5 Android作業系統介紹 24
2.5.1 系統架構 25
2.5.2 生命週期 27
第三章、 手勢識別系統架構與設計 30
3.1 系統架構 30
3.2 手勢識別演算法 31
3.2.1 手勢識別演算法 31
3.3 特徵值計算 39
3.4 模糊規則制定 41
3.5 開發環境 54
3.5.1 硬體 55
3.5.2 軟體 55
第四章、 實驗結果與分析 57
4.1 實驗設備 57
4.2 實驗結果 59
第五章、 結論與未來工作 68
5.1 結論及未來工作 68
參考文獻 70


表目錄
表2-1、Activity定義生命週期的7種方法 .................................... 28
表4-1、設備 ...................................................................................... 57
表4-2、手勢識別結果 ...................................................................... 60
表4-3、HTC New One識別結果 ..................................................... 61
表4-4、ASUS MEMO Pad識別結果............................................... 64


圖目錄
圖 2-1、Wii內部結構 ........................................................................ 8
圖 2- 2、光學感應條 .......................................................................... 9
圖2-3、主成分分析 .......................................................................... 13
圖2-4、類神經網路架構圖 .............................................................. 15
圖2-5、支持向量機示意圖 .............................................................. 16
圖2-6、動態時間校正示意圖 .......................................................... 18
圖2-7、三角形歸屬函數 .................................................................. 21
圖2-8、梯形歸屬函數 ...................................................................... 21
圖2-9、高斯形歸屬函數 .................................................................. 22
圖2-10、單值歸屬函數 .................................................................... 23
圖2-11、Android系統架構圖 .......................................................... 26
圖2-12、Activity生命週期示意圖 .................................................. 29
圖3-1、系統架構圖 .......................................................................... 31
圖3-2、手勢識別演算法流程圖 ...................................................... 32
圖3-3、模糊推論基本架構 .............................................................. 36
圖3-4、x軸、y軸和z軸加速度特徵值之對應歸屬函數 ............. 36
圖3-5、G輸出對應歸屬函數 .......................................................... 37
圖3-6、推論引擎示意圖 .................................................................. 37
圖3-7、解模糊化示意圖 .................................................................. 38
圖3-8、模糊分類器模擬 .................................................................. 38
圖3-9、手勢動作四在x軸上之正規化加速度值 .......................... 40
圖3-10、手勢動作四在x軸加速度特徵 ........................................ 41
圖3-11、手勢動作............................................................................. 42
圖3-12、手勢動作1在x軸加速度特徵 ........................................ 43
圖3-13、手勢動作1在 y軸加速度特徵 ....................................... 44
圖3-14、手勢動作1在 z軸加速度特徵 ........................................ 45
圖3-15、手勢動作2在x軸加速度特徵 ........................................ 46
圖3-16、手勢動作2在y軸加速度特徵 ........................................ 47
圖3-17、手勢動作2在z軸加速度特徵 ......................................... 48
圖3-18、手勢動作3在x軸加速度特徵 ........................................ 49
圖3-19、手勢動作3在y軸加速度特徵 ........................................ 50
圖3-20、手勢動作3在z軸加速度特徵 ......................................... 51
圖3-21、手勢動作4在x軸加速度特徵 ........................................ 52
圖3-22、手勢動作4在y軸加速度特徵 ........................................ 53
圖3-23、手勢動作4在z軸加速度特徵 ......................................... 54
圖4-1、SKY Vega A820L ................................................................. 58
圖4-2、HTC New One ...................................................................... 58
圖4-3、ASUS MEMO Pad ................................................................ 59
圖4-4、手勢動作1在z軸加速度特徵(使用HTC New One) ....... 62
圖4-5、手勢動作2在y軸加速度特徵(使用HTC New One) ...... 62
圖4-6、手勢動作3在y軸加速度特徵(使用HTC New One) ...... 63
圖4-7、手勢動作4在z軸加速度特徵(使用HTC New One) ....... 63
圖4-8、手勢動作1在y軸加速度特徵(使用ASUS MEMO Pad) 65
圖4-9、手勢動作2在y軸加速度特徵(使用ASUS MEMO Pad) 66
圖4-10、手勢動作3在z軸加速度特徵(使用ASUS MEMO Pad) ............................................................................................................. 66
圖4-11、手勢動作4在y軸加速度特徵(使用ASUS MEMO Pad) ............................................................................................................. 67

[1]J. P. Dai , J. Teng, X. Bai, Z. Shen and D. Xuan, “Mobile Phone Based Drunk Driving Detection, ” 2010 4th conference on international Pervasive computing technologies for Healthcare, pp. 1-8, Mar. 22-25, 2010.

[2]H. Ghasemzadeh and R. Jafari, “Coordination Analysis of Human Movements With Body Sensor Networks： A Signal Processing Model to Evaluate Baseball Swings,” IEEE on Sensors Journal, Vol. 11, No. 3, pp. 603-610, Mar. 2011.

[3]H. C. Chu and Y. C. Cheng, “Design and Implementation of an Intuitive Gesture Recognition System Using a Hand-held Device,” International Conference in Electrics, Communication and Automatic Control, pp. 18-20, Jun. 23-24, 2011.

[4]T. D. Bui and L. T. Nguyen, “Recognizing Postures in Vietnamese Sign Language With MEMS Accelerometers,” IEEE Sensors Journal, Vol. 7, No. 5, pp. 707-712, May 2007.

[5]Y. W. Bai, S. C. Wu and C. L. Tsai, “Design and Implementation of a Fall Monitor System by Using a 3-Axis Accelerometer in a Smart Phone,” IEEE Transactions on Consumer Electronics, Vol. 58, No. 4, pp.1269-1275, Nov. 2012.

[6]H. C. Chu, C. A. Hsu, Y. Z. Zhang and F. J. Hsieh, “Gesture Admission Control System,” International Conference on Advanced Information Technologies (AIT 2010), Apr. 23-24, 2010.

[7]H. C. Chu, Z. F. Yang and C. C. Chou, “Design and Implementation of Gesture Admision Contorl System Using Hand-held Device,” International Conference on Advanced Information Technologies (AIT 2012), Apr. 27-28, 2012.

[8]J. Lin, H. Nishino, T. Kagawa and K. Utsumiya, “A method of two-handed gesture interactions with applications based on commodity devices,” Computers & Mathematics with Applications, pp. 448–457, Vol. 63, No. 2, 2012.

[9]R. Xu , S. Zhou and W. J. Li, “MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition,” IEEE Sensors Journal, Vol. 12, No. 5, May 2012.

[10]K. Murao, T. Terada, Y. Ai and R. Matsukura, “Evaluating Gesture Recognition by Multiple-Sensor-Containing Mobile Devices,” 2011 15th Annual International Symposium on Wearable Computers (ISWC), pp.55-58, Jun. 12-15, 2011.

[11]P. W. Chen, K. S. Ou and K. S. Chen, “IR Indoor Localization and Wireless Transmission for Motion Control in Smart Building Applications based on Wiimote Technology,” Proceedings of SICE Annual Conference, pp. 1781-1785, Aug. 18-21, 2010.

[12]T. Schlomer, B. Poppinga, N. Henze and S. Boll, ” Gesture Recognition with a Wii Controller,” Proceedings of the 2nd international conference on Tangible and embedded interaction pp. 11-14, Feb. 18-20, 2008.

[13]T. Marasovic and V. Papic, “Accelerometer-Based Gesture Classification Using Principal Component Analysis,” 2011 19th International Conference on Software, Telecommunications and Computer Networks (SoftCOM), pp.1-5, Sep. 15-17, 2011.

[14]J. S. Wang and F. C. Chuang, “An Accelerometer-Based Digital Pen With a Trajectory Recognition Algorithm for Handwritten Digit and Gesture Recognition,” IEEE Transactions on Industrial Electronics, Vol.59, No. 7, pp.2998-3007, Jul. 2012.

[15]D. F. Specht, “Neural Networks,” Neural Networks, Vol. 3. pp. 109 -118, 1990.

[16]C. Doukas and I. Maglogiannis, “Advanced Patient or Elder Fall Detection based on Movement and Sound Data,” Second International Conference on Pervasive Computing Technologies for Healthcare, pp. 103-107, Jan. 30-Feb. 1, 2008.

[17]A. Akl, F. Chen and S. Valaee, “A Novel Accelerometer-Based Gesture Recognition System,” IEEE Transactions on Signal Processing, Vol. 59, No. 12, pp. 6197-6205, Dec. 2011.

[18]I. T. Jolliffe,” Principal Component Analysis,” 2002, XXIX, ISBN 9780387954424.

[19]王進德，“類神經神經網路與模糊控制理論入門與應用”，全華圖書股份有限公司，2008，ISBN 9789572155943。

[20]S. Heykin, “Neural Networks-A Comprehensive foundation,” Prentice Hall, 2000, ISBN 0-13-273350-1

[21]C. S. Myers, and L. R. Rabiner, “A comparative study of several dynamic time-warping algorithms for connected word recognition.” The Bell System Technical Journal, Vol. 60, No. 7, pp.1389-1409, Sep. 1981.

[22]L. X. Wang著，汪惠健譯，“模糊理論與應用”，臺灣培生教育出版公司，2006
[23]Open Handset Alliance, http：//www.openhandsetalliance.com/index.html

[24]維基百科-Android, http：//zh.wikipedia.org/wiki/Android

[25]Android Developers, http：//developer.android.com/index.html

[26]黃彬華，“Android 2.X手機程式開發教戰手冊” ，碁峰資訊股份有限公司， 2011，ISBN 9789862761861。