簡易檢索 / 詳目顯示

回結果列表
研究生： 李坤鴻
Kun-Hong Li
論文名稱： 基於加速儀之動作分類研究
Study on Accelerometer based Motion Classification
指導教授： 蘇順豐
Shun-Feng Su
口試委員: 王偉彥
none
郭重顯
none
王文俊
none
學位類別： 碩士
Master
系所名稱： 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年： 2010
畢業學年度： 98
語文別： 英文
論文頁數： 90
中文關鍵詞： 手勢分類 、函數型類神經網路 、相似度測量 、加速度儀 、Wii遙控器
外文關鍵詞： Similarity measure, Functional Artificial Neural Networks (FANN), Motion classification, Accelerometer, Wii Remote
相關次數： 點閱：219  下載：4 
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

    • 在本論文中，我們利用Wii遙控器去擷取動作的加速度軌跡用於手勢辨識。我們的研究目的是研究使用Wii遙控器去擷取使用者的手勢，這些手勢可以被設定為控制命令並用於控制電子裝置。首先，我們提出一個使用於函數型類神經網路(Functional Artificial Neural Network, FANN)相似度測量的方法來進行手勢分類。在本研究的第二個問題中，我們將利用函數型類神經網路函數映射(functional mapping)的特性去處理手勢分類問題。原來的函數可能不容易處理，如果能將原函數經由函數型類神經網路映射到我們容易處理的函數，則系統或許能有更準確的分類結果。由於我們的實作考慮的是2維加速度軌跡，所以我們這裡提出一個結合二相似度測量的方法。這種融合的方法是根據於模糊理論的概念與高斯函數用來描述模糊歸屬度函數。實驗結果顯示，本系統手勢分類辨識率為96%。而使用函數型類神經網路於手勢分類的效果似乎沒有幫助，這個問題則需要在未來被更細心的研究。

    In this thesis, we reported our study of using a Wii Remote to capture the acceleration trajectories of motions, where those motions are considered as gestures for recognition. The aim of this study is to investigate the use of a Wii Remote in capturing motions made by a user so that those motions can be recognized to define some control commands for control electronic devices. A direct approach of motion classification with the use of some similarity measure used in Functional Artificial Neural Networks (FANN) will be proposed first. The second issue considered in our study is the use of the functional mapping characteristic of FANN for the motion classification problem. The original functions may not be easy to handle and then if the original function can be mapped by FANN to another functions which is easy to handle, the system may have more accurate classification for motions. Since two dimensional trajectories are considered in our experiments, a way of combining those two similarity measures is proposed. This measure fusion approach is based on the fuzzy concept and Gaussian functions are used to characterize the fuzzy membership functions. Our experiments show that the recognition rate of the proposed motion classification is about 96%. The use of FANN seems not helpful. A more subtle study is needed for this issue.

    摘要 I Abstract II 誌謝 III Contents IV List of Tables VI List of Figures VII Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Background 2 1.3 Research Ideas 4 1.4 Organization of the Thesis 5 Chapter 2 Functional Artificial Neural Networks 6 2.1 Volterra Series 6 2.2 Functional Artificial Neural Networks 8 2.2.1 Single-input Single-output Nonlinear Systems 11 2.2.2 Multi-inputs Multi-outputs Nonlinear Systems 14 2.3 Chapter Summary 16 Chapter 3 Hardware and Software Used 17 3.1 Wii Remote 17 3.2 Glove Programmable Input Emulator 19 3.3 Chapter Summary 23 Chapter 4 Motion Classification 24 4.1 Introduction 24 4.2 Experimental Framework 25 4.3 Data pre-processing 26 4.3.1 Axis-Correction 27 4.3.2 Up-Sampling 30 4.3.3 Normalization 32 4.4 Experimental Data 35 4.5 Similarity Measure 39 4.6 Fuzzy Fusion of Similarities 41 4.7 Experimental Results of Motion Classification 42 4.8 Chapter Summary 45 Chapter 5 Motion Classification with FANN 53 5.1 Introduction 53 5.2 Desired Outputs of FANN 54 5.2.1 Position Data 54 5.2.2 Graphic Image Data 57 5.3 Motion Classification with FANN 59 5.4 Experiment of FANN 64 5.4.1 Output of FANN 64 5.4.2 Motion Classification with FANN 68 5.5 Chapter Summary 73 Chapter 6 Conclusions and Further Research 84 6.1 Conclusions 84 6.2 Further Research 85 References 86

    [1] K. H. Chon, N. H. Holstein-Rathlou, D. J. Marsh, and V. Z. Marmarelis, "Comparative nonlinear modeling of renal autoregulation in rats: Volterra approach versus artificial neural networks," IEEE Trans. on Neural Networks, vol. 9, no.3, pp. 430-435, May 1998.
    [2] H. Lin, "Identification of spinal deformity classification with total curvature analysis and artificial neural network," IEEE Trans. on Biomedical Engineering, vol.55, pp. 376 – 382, Jan. 2008.
    [3] M. Martinez-Ramon, J. L. Rojo-Alvarez, G. Camps-Valls, J. Munoz-Mari, A. Navia-Vazquez, E. Soria-Olivas, and A. R. Figueiras-Vidal, , "Support vector machines for nonlinear kernel ARMA system identification," IEEE Trans. on Neural Networks, vol.17, pp. 1617 - 1622, Nov. 2006.
    [4] W. Yu and X. Li, "Fuzzy identification using fuzzy neural networks with stable learning algorithms," IEEE Trans. on Neural Networks, vol.12, pp. 411 - 420, June 2004.
    [5] S. Kamalasadan and A. A. Ghandakly, "A neural network parallel adaptive controller for dynamic system control," IEEE Trans. on Instrumentation and Measurement, vol. 56, pp. 1149 – 1159, July 2007.
    [6] C. F. Hsu, "Self-organizing adaptive fuzzy neural control for a class of nonlinear systems," IEEE Trans. on Neural Networks, vol.18, pp. 1232 – 1241, July 2007.
    [7] B. J. Yang and A. J. Calise, "Adaptive control of a class of nonaffine systems using neural networks," IEEE Trans. on Neural Networks, vol.18, pp. 1149 – 1159, July 2007.
    [8] S. S. Ge, G. Y. Li, J. Zhang, and T. H. Lee, "Direct adaptive control for a class of MIMO nonlinear systems using neural networks," IEEE Trans. on Automatic Control, vol.49, pp. 2001 – 2006, Nov. 2004.
    [9] T. Auld, A. W. Moore, and S. F. Gull, "Bayesian neural networks for internet traffic classification," IEEE Trans. on Neural Networks, vol.18, pp. 223 – 239, Jan. 2007.
    [10] I. Maglogiannis, H. Sarimveis, C. T. Kiranoudis, A. A. Chatziioannou, N. Oikonomou, and V. Aidinis, "Radial basis function neural networks classification for the recognition of idiopathic pulmonary fibrosis in microscopic images," IEEE Trans. on Information Technology in Biomedicine, vol.12, pp. 42 - 54, Jan. 2008.
    [11] Y. J. Oyang, S. C. Hwang, Y. Y. Ou, C.Y. Chen, and Z. W. Chen, "Data classification with radial basis function networks based on a novel kernel density estimation algorithm," IEEE Trans. on Neural Networks, vol.16, pp. 225 - 236, Jan. 2005.
    [12] T. McConaghy, H. Leung, E. Bosse, and V. Varadan, "Classification of audio radar signals using radial basis function neural networks," IEEE Trans. on Instrumentation and Measurement, vol.52, pp. 1771 - 1779, Dec. 2003.
    [13] S. Mishra, C. N. Bhende, and B. K. Panigrahi, "Detection and classification of power quality disturbances using s-transform and probabilistic neural network," IEEE Trans. on Power Delivery, vol.23, pp. 280 - 287, Jan. 2008.
    [14] K. E. Graves and R. Nagarajah, "Uncertainty estimation using fuzzy measures for multiclass classification," IEEE Trans. on Neural Networks, vol.18, pp. 128 - 140, Jan. 2007.
    [15] L. Rutkowski, "Adaptive probabilistic neural networks for pattern classification in time-varying environment," IEEE Trans. on Neural Networks, vol.15, pp. 811 - 827, July 2004.
    [16] S. Munder and D. M. Gavrila, "An experimental study on pedestrian classification," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol.28, pp. 1863 - 1868, Nov. 2006.
    [17] G. Camps-Valls, L. Gomez-Chova, J. Calpe-Maravilla, J. D. Martin-Guerrero, E. Soria-Olivas, L. Alonso-Chorda, and J. Moreno, "Robust support vector method for hyperspectral data classification and knowledge discovery," IEEE Trans. Geoscience and Remote Sensing, vol.42, pp. 1530 - 1542, July 2004.
    [18] R. K. Begg, M. Palaniswami, and B. Owen, "Support vector machines for automated gait classification," IEEE Trans. on Biomedical Engineering, vol.52, pp. 828 - 838, May 2005.
    [19] M. Schetzen, "The Volterra and Wiener Theories of Nonlinear Systems," New York, Wiley, 1980.
    [20] M. Iatrou, T. W. Berger, and V. Z. Marmarelis, "Modeling of nonlinear nonstationary dynamic systems with a novel class of artificial neural networks," IEEE Trans. on Neural Networks, vol. 10, no. 2, pp. 327-339, March 1999.
    [21] V. Z. Marmarelis and X. Zhao, "Volterra models and three-layer perceptrons," IEEE Trans. Neural Networks, vol. 8, pp.1421-1433, 1997.
    [22] G. W. Davis and M. L. Gasperi, "ANN modeling of Volterra systems," Proc. IEEE Conf. Decision and Control, pp. 20-31, 1990.
    [23] L. V. Zyla and R. J. P. de Figueiredo, "Nonlinear system identification based on a Fock space framework," SIAM J. Contr. Optimization, vol. 21, no. 6, pp. 931-939, Nov. 1983.
    [24] R. W. Newcomb and R. J. P. de Figueiredo, "A multi-input multi-output functional artificial neural network," Journal of Intelligence and Fuzzy Systems vol.4 No.3, pp 207-213, 1996.
    [25] D. A. Panagiotopoulos, S. K. Singh, T. R. Darden, and R. W. Newcomb, "Hardware oriented semistate descriptions of functional artificial neural networks," IEEE Trans. on Neural Networks, vol. 2, pp. 1197-1198, 1994.
    [26] R. W. Newcomb and R. J. P. deFigueiredo, "A functional artificial neural network," Proceddings of the 3rd International Conference on Automation, Robotics, and Computer Vision, Singapore, pp. 566-570, Nov. 1994.
    [27] R. J. P. de Figueiredo, "The OI, OS, OMNI, and OSMAN networks as best approximations of nonlinear systems under training data constraints," Proceedings of IEEE ISCAS ’96, Atlanta, pp. 349-352, May 1996.
    [28] R. J. P. de Figueiredo, "Optimal interpolating and smoothing functional artificial neural networks (FANN’s) based on a generalized Fock space framework," IEEE Trans. Circuits Syst. Signal Processing, vol. 17, no 2, pp.271-287, 1998.
    [29] D. A. Panagiotopoulos, R. W. Newcomb, and S. K. Singh, "Planning with a functional neural-network architecture," IEEE Trans. on Neural Networks, vol. 10, no. 1, pp. 115-127, January 1999.
    [30] D. A. Panagiotopoulos, S. K. Singh, and R. W. Newcomb, "VLSI implementation of a functional neural network," IEEE International Symposium on Circuits and Systems, Hong Kong, pp. 701-704, June 1997.
    [31] R. J. P. de Figueiredo and T. Eltoft, "A DCT-Based D-FANN for nonlinear adaptive time series prediction," IEEE tractions on Circuits and System-II: Analog and Digital Signal Processing, vol.47, no. 10, pp. 1131-1134, Oct. 2000.
    [32] M. Davy, C. Doncarli, G.F. Boudreaux-Bartels, " Improved optimization of time-frequency-based signal classifiers," IEEE Signal Processing Letters, vol.8, no. 2, pp. 52-57, Feb. 2001.
    [33] V. Venkatachalam, J. L. Aravena, " Nonstationary signal classification using pseudo power signatures: The matrix SVD approach," IEEE tractions on Circuits and System-II: Analog and Digital Signal Processing, vol. 46, no. 12, pp. 1497-1505, Dec. 1999.
    [34] http://gdr-isis.org/tftb/tutorial/node10.html, Non stationary signals, GdR ISIS
    [35] http://www.nintendo.com/consumer/downloads/WiiChEng.pdf, Wii Operations Manual.
    [36] http://carl.kenner.googlepages.com/glovepie, GlovePIE.

    QR CODE