標誌點式人體運動追蹤系統中常必須處理的問題有兩部分，第一部分為如何將不同的標誌點群對應至所屬之人體連桿；第二部分是如何得知標誌點和人體連桿間的相對位置。而目前大部分研究中用於運動重現之人體模型通常為電腦建構的虛擬骨架，亦或是來源為資料庫並非受測者本身的模型。因此，本文將利用已經過結構化處理的人體掃描點來建立三個多自由度人體連桿模型，並應用於運動重現。其中，模型I具有21個連桿及126個運動自由度；模型II具有23個連桿及48個關節自由度；而模型III則具有55個連桿及90個關節自由度。

本文亦使用圖形編碼的編碼標誌框進行運動追蹤，並根據編碼的特性去處理哪些標誌點該對應哪個人體連桿的問題；此外，利用標誌框自動化註冊至人體連桿的方法，得到兩者之間的相對位置關係；最後，藉由追蹤標誌框的運動並透過自動化註冊參數，將標誌框的位置轉換成每個連桿之確切位置，然後再以受測者本身的人體模型進行運動重現，達到追蹤人體動態運動之目的。

There are two major problems to be solved in the marker-based human motion tracking system. One is how the different markers on the space can correspond to the specific links of a human model, and the other is how the relative position between the marker and its corresponding link can be figured out. In most of the current studies, the human models used for motion replication are acquired from the database or constructed by the virtual skeletons. However, the study focuses on constructing three human models with multi-degree of freedom (DOF) links through the structured points of a subject, and these models can be used to replicate the motions of the human body. Human Model I has 21 links and 126 relative DOFs, Human Model II has 23 links and 45 joint DOFs, and Human Model III has 55 links and 90 joint DOFs.

The study also uses Coded Markers designed with geometric shape to track the motions of the human body, and the property of coding can be used to solve the problem of how the markers can correspond to the corresponding human links. With the method of Auto-registration, we can acquire the relative positions between the markers and the links. Besides, we can switch the information of the markers to the actual positions of the links by registering the markers on the links of the human body in the process of motion tracking. Last, the tracked motions of the human body can be replicated with our constructed human model.

1. Ahmad, R., M. S. Yaacob and M. B. Che Omar, “Kinematics Modeling of Human Motion Using System Identification Technique,” in Modeling & Simulation, 2008. AICMS 08. Second Asia International Conference on, pp. 338-343, 2008.
2. Ahmad, R., M. Omar, M. S. Yaacob, M. Hussein, M. Zain and M. Y. Abdullah, “Modeling of human motion through motion captured data,” in Information Technology, 2008. ITSim 2008. International Symposium on. Vol. 3, pp. 1-7, 2008.
3. Analysis, M., Raptor-4 Digital RealTime System. 2009 [cited；Available from：http://www.motionanalysis.com/index.html].
4. Certain, A. and W. Stuetzle, “Automatic body measurement for mass customization of garments,” in 3-D Digital Imaging and Modeling, 1999. Proceedings. Second International Conference on, pp. 405-412, 1999.
5. Herda, L., P. Fua, R. Plankers, R. Boulic and D. Thalmann, “Skeleton-based motion capture for robust reconstruction of human motion,” in Computer Animation 2000. Proceedings, pp. 77-83, 2000.
6. Hui, Y., Q. Sheng-feng, D. K. Wight and K. Jin-sheng, “Generation of 3D Human Models with Different Levels of Detail through Point-Based Simplification,” in EUROCON, 2007. The International Conference on “Computer as a Tool”, pp. 1982-1986, 2007.
7. Kolahi, A., M. Hoviattalab, T. Rezaeian, M. Alizadeh, M. Bostan and H. Mokhtarzadeh, “Design of a marker-based human motion tracking system,” Biomedical Signal Processing and Control, vol. 2, pp. 59-67, 2007.
8. Kunju, N., N. Kumar, D. Pankaj, D. P. Singh and A. Kumar, “Algorithm for Kinematic Measurements using Active Markers,” in Advance Computing Conference, 2009. IACC 2009. IEEE International, pp. 387-391, 2009.
9. Kulic, D. and Y. Nakamura, “Comparative study of representations for segmentation of whole body human motion data,” in Intelligent Robots and Systems, 2009. IROS 2009. IEEE/RSJ International Conference on, pp. 4300-4305, 2009.
10. Maletsky, L. P., J. Sun and N. A. Morton, “Accuracy of an optical active-marker system to track the relative motion of rigid bodies,” Journal of biomechanics, vol. 40, pp. 682-685, 2007.
11. NorthernDigitalInc, Optotrak Certus. 2007 [cited；Available from：http://www.ndigital.com].
12. Optimed, K., KOWA LMVZ. 2008 [cited；Available from：http://www.kowa-usa.com/frontend/default.asp].
13. PhoenixTechnologiesInc, Visualeyez. 2007 [cited；Available from：http://www.ptiphoenix.com/].
14. Robinette, K. M., H. Daanen and E. Paquet, “The CAESAR project: a 3-D surface anthropometry survey,” in 3-D Digital Imaging and Modeling, 1999. Proceedings. Second International Conference on, pp. 380-386, 1999.
15. Ringer, M. and J. Lasenby, “A procedure for automatically estimating model parameters in optical motion capture,” Image and Vision Computing, vol. 22, pp. 843-850, 2004.
16. Sandip Sar Dessai, A. Hornung, L. Kobbelt and J. Herder, “Automatic Data Normalization and Parameterization for Optical Motion Tracking,” Journal of Virtual Reality and Broadcasting, vol. 3, 2006.
17. SENTECH, STC-CL232A. 2008 [cited；Available from：http://www.sentechamerica.com/].
18. Tanie, H., K. Yamane and Y. Nakamura, “High Marker Density Motion Capture by Retroreflective Mesh Suit,” in Robotics and Automation, 2005. ICRA 2005. Proceedings of the 2005 IEEE International Conference on, pp. 2884-2889, 2005.
19. Webster, R. W. and W. Yong, “An intelligent mobile robot golfing system using binocular stereo vision,” in Pattern Recognition, 1992. Vol.I. Conference A: Computer Vision and Applications, Proceedings., 11th IAPR International Conference on, pp. 603-607, 1992.
20. 陳治評，「自動化人體計測與電腦人模之建立」，國立成功大學機械工程學系碩士論文，2003。
21. 黃稚桓，「具雙重模式光學三維量測系統之建構」，國立成功大學機械工程學系碩士論文，2009。