本論文係探討利用人類自行定義的手勢動作與小型人形機器人做互動。首先描述機器人的硬體架構及其電路設計，並說明如何設計人機介面，用其來調整與設定機器人之動作，並將最後規劃完成的動作序列儲存在動作資料庫中。接著介紹戴在人手腕上的 IMU 6-DOF感測器，此感測器可藉由藍芽與電腦通訊，傳回傾斜角速度，滾動角速度，旋轉角速度，及三軸傾斜角度等資訊，透過實作在人機介面上的辨識演算法，解譯出人類手勢動作所代表的意含，並將此辨識結果經由Zigbee傳送給小型人形機器人以做出相對應的動作。本論文中使用自適應中值濾波器對訊號做濾波，經由語音的框架法擷取出動態手勢的特徵，並實作動態規劃中的最長共同子序列演算法來對動態手勢進行辨識，並且提出改進的方法。最後，藉由實驗之結果，來驗證所提方法之效能及適用性。

This thesis explores how to interact with the Humanoid robot using the user defined hand gesture. First, the hardware architecture and the circuit design are described, and we explain how to design human-computer interface, and to regulate and set the motion of humanoid robot. And then the planning motion sequences are stored in the motion database. Second, the Inertial Measurement Unit (IMU) 6-DOF sensor worn on the wrist of human being is introduced. The sensor can communicate with computer through Bluetooth, and send back the data of Pitch, Roll, Yaw, and tilt angle of the three axes. Then we can interpret the meaning of hand gestures through the recognition algorithm implemented in the computer. The recognition result will be sent to the humanoid robot to do the corresponding movements. In this thesis, we filter the signal by adaptive median filter firstly and then extract the features of the dynamic hand gesture by the frame method which is often used in speech. And then we implement the Longest Common Subsequence (LCS) algorithm of dynamic programming to recognize the dynamic hand gesture. Finally, all the experiments verify the performance and the feasibility of the proposed system.

References
[1] X. Yin and X. Zhu, “Hand posture recognition in gesture-based human-robot interaction,” in Proceedings of IEEE Conference on Industrial Electronics and Applications, pp. 1-6, May 24-26, 2006.
[2] H. Ishiguro, T. Ono, M. Imai, and T. Kanda, “Development and evaluation of an interactive humanoid robot-Robovie,” in Proceedings of ICRA IEEE International Conference on Robotics and Automation, vol. 2, pp. 1848-1855, Washington DC, 2002.
[3] T. Kanda, R. Sato, N. Saiwaki, and H. Ishiguro, “A two-month field trialin an elementary school for long-term human–robot interaction,” IEEE Transaction Robot, vol. 23, no. 5, pp. 962–971, Oct. 2007.
[4] J. Fahrenberg, F. Foerster, M. Smeja, and W. M‥uller, “Assessment of posture and motion by multichannel piezoresistive accelerometer recordings,” Psychophysiol., vol. 34, pp. 607–612, 1997.
[5] F. Foerster and J. Fahrenberg, “Motion pattern and posture: Correctly assessed by calibrated accelerometers,” Behav. Res. Meth. Instrum. Comput., vol. 32, pp. 450–457, 2000.
[6] P. H. Veltink, H. B. Bussmann, W. de Vries, W. L. Martens, and R.C. van Lummel, “Detection of static and dynamic activities using uniaxial accelerometers,” IEEE Trans. Rehabil. En., vol. 4, no. 4, pp. 375–385, Dec. 1996.
[7] D. M. Karantonis, M. R. Narayanan, M. Mathie, N. H. Lovell, and B. G. Celler, “Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring,” IEEE Transactions on Information Technology in Biomedicine, vol. 10, pp. 156-167, Jan. 2006.
[8] http://www.robotis.com/zbxe/main
[9] http://www.robotis.com/zbxe/software_en
[10] M. K. Bhuyan, D. Ghosh, and P. K. Bora, “Hand motion tracking and trajectory matching for dynamic hand gesture recognition,“ Journal of Experimental & Theoretical Artifical Intelligence, vol. 18, Issue 4, pp. 435-447, 2006.
[11] M. H. Yang, N. Ahuja, and M. Tabb, “Extraction of 2D Motion Trajectories and Its Application to Hand Gesture Recognition,” IEEE Transactions on Pattern Anaylysis and Machine Intelligence, vol. 24 n. 8, pp. 1061-1074, August 2002.
[12] D. J. Sturman and D. Zeltzer, “A survey of glove-based input,” Computer Graphics and Application.IEEE, vol. 14 Issue: 1, Jan. 1994, pp. 30-39, 1994.
[13] C. C. Shih, Evaluation and Simulation of the Hand, Master Thesis in Industrial Design, National Cheng Kung University, Tainan, Taiwan, 2000.
[14] B.W. Min and H. S. Yoon, “Hand gesture recognition using hidden Markov models systems,” in Proceeding of the IEEE International Conference on Computational Cybernetics and Simulation, vol. 5, pp. 4232-4235, 1997.
[15] M. Brand, N. Olive, and A. Pentland, “Coupled hidden markov models for complex action recognition,” Computer Vision and Pattern Recognition, 1997.
[16] D. Ramanan and D. A. Forsyth, “Automatic annotation of everyday movements,” Neural Information Processing Systems, 2003.
[17] O. Arikan, D. A. Forsyth, and J. O’ Brien, “Motion synthesis from annotations,” ACM Transaction on Graphics, vol.33, no. 3, pp. 402-408, 2003.
[18] J. Weissmann and R. Salomon, “Gesture recognition for virtual reality applications using data gloves and neural networks,” in Proceeding of the International Joint Conference on Neural Networks, vol.3, pp. 2043-2046, 1999.
[19] T. B. Moeslund and E.Granum, ”A survey of computer vision-based human motion capture,” Computer Vision and Image Understanding, vol. 81, no. 3, pp. 231-268, 2001.
[20] L. Wang, W. Hu and T. Tan, “Recent developments in human motion analysis,” Pattern Recognition, vol. 36, no. 3, pp. 585-601, 2003.
[21] Y. Luo, T. D. Wu and J. N. Hwang, ”Object based analysis and interpretation of human motion in sports video sequences by Dynamic Bayesian Networks,” IEEE Computer Vision and Image Understanding, vol. 92, no. 2, pp. 196-216, 2003.
[22] M. C. Su, Y. X. Zhao, H. Huang, and H. F. Chen, “A Fuzzy Rule-Based Approach to Recognizing 3D Arm Movements,” IEEE Trans. on Neural Systems and Rehabilitation Engineering, vol. 9, no. 2, pp. 191-201, 2001.
[23] H. Hwang and R. A. Haddad, “Adaptive median filters: New algorithms and results,” IEEE Transaction on Image Processing, vol. 4, pp. 499-502, April 1995.
[24] V.V. Khryashchev, A.L. Priorov, I.V. Apalkov and P.S. Zvonarev, ”Image denoising using adaptive switching median filter,” in Proceedings of 2005 IEEE International Conference on Image Processin,g vol. 1, pp. 11-14 Sept. 2005.
[25] H. L. Eng and K. K. Ma, “Noise adaptive soft-switching median filter,” IEEE Transactions on Image Processing, vol.10, no.2, pp. 242-251, 2001.
[26] Y. Zhao, D. Li, and Z. Li, “Performance enhancement and analysis of an adaptive median filter,” in Proceeding of the IEEE International Conference on Communications and networking, pp. 651-653, 2007.
[27] C. C. Shien, Measuring Dynamic Similarities for Chinese Signatures by Continuous Hidden Markov Models, Master Thesis in Electrical Engineering, Yuan Ze University, Taiwan, 1997.
[28] D. J. Berndt and J. Clifford, “Using dynamic time warping to find patterns in time series,” Working Notes of the Knowledge Discovery in Databases Workshop, pp. 359-370, 1994.
[29] A. Corradini, “Dynamic Time Warping for Off-Line Recognition of a Small Gesture Vocabulary,” in Proceedings of IEEE ICCV Workshop Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS ‘01), pp. 82-89, July 2001.
[30] K. J. Wang, A Comprehensive Study on Intelligent Glove-Based Hand Gesture Recognition, Master thesis in Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan, 2000.
[31] C. M. Chung, Accelerometer Based Gesture Recognition System and Its Application, Master Thesis in Engineering Science, National Cheng Kung University, Tainan, Taiwan, 2008.
[32] D. S. Hirschberg, “A Linear Space Algorithm for Computing Maximum Common Subsequences,” Comm. of the ACM, vol.18, no.6, pp. 341-343, 1975.
[33] http://en.wikipedia.org/wiki/Longest_common_subsequence_problem