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研究生:謝國爗
研究生(外文):Hsieh, Kuo-Yeh
論文名稱:MultipleSinkPlacementforMotionCapture:TakingRehabilitationasanExample
論文名稱(外文):動作擷取應用之多接收點佈建方法:以復健系統為例
指導教授:金仲達金仲達引用關係
指導教授(外文):King, Chung-Ta
學位類別:碩士
校院名稱:國立清華大學
系所名稱:資訊系統與應用研究所
學門:電算機學門
學類:系統設計學類
論文種類:學術論文
畢業學年度:97
語文別:英文
論文頁數:47
中文關鍵詞:多接收點佈置方法覆蓋問題無線人體區域感測網路直視路徑傳輸
外文關鍵詞:Multiple sink placementCoverage problemWireless body sensor networksLine-of-sight transmission
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無線人體區域感測網路(WBSN)的技術已漸趨成熟,未來更將成為另一項人體動作捕捉(motion capture)應用的新技術,透過佈置在人體上的慣性感測器(inertial sensor)所收集的加速度、角加速度等資訊,可以進一步地分析並還原人體的動作表現,其應用範圍相當廣泛,諸如復健、健身與電腦動畫等系統的開發。
然而,無線訊號在此感測網路當中,相當容易受到人體的阻隔所影響,致使無線網路封包傳送的成功率大幅下降,同時也影響到動作還原計算的正確性,加上諸如以上應用,整體系統的運作壽命亦會大大影響使用者的使用意願,所以,如何在不需要額外消耗電量的情況下,同時確保封包傳送的成功率,將成為很重要的議題。本篇論文針對此問題以實驗證實:於無線人體區域感測網路中,在傳統單一接收點(single sink)的架構底下,是無法避免人體遮蔽狀況的,因而提出了一個佈置多個接收點(multiple sink)的新架構。
論文中所提出的多接收點(multiple sink)架構,主要針對固定區域的人體動作應用,如:復健與健身,而此佈置多個接受點的問題,同時也是個立體空間的覆蓋問題(coverage problem);接著我們將此覆蓋問題轉換至平面的覆蓋問題上,並進一步利用啟發式演算法(heuristic algorithm)求得所需佈置之接收點的最少數量與位置。
最後透過實作的復健系統評估以上方法,結果顯示出本篇論文所提出的解法,在復健應用之下,確實能有效提升封包傳送的成功率,而且整體的平均成功率可達幾近100%。
Wireless body sensor network (WBSN) enables a new generation of motion capture systems for applications such as rehabilitation, fitness, or gaming. However, the body might obstruct the transmission of wireless signals, which significantly reduces the transmission success rate and thus degrades the quality of motion reconstruction. This thesis proves that deploying a single sink device cannot avoid such body fading effect. There are various ways to solving this problem. In this thesis, we focus on solutions that do not incur extra energy consumption in the sensor nodes in order to sustain the lifetime of the system. We consider the type of applications in which user movements are confined within a fixed region and their intended motions are known, e.g. rehabilitation exercises. The above problem can be solved by placing multiple sink devices around the user to collect the sensed data. This sink placement problem can be modeled as a coverage problem in 3D space, and we propose a heuristic algorithm to determine the minimum number and placement of the sink devices. Experimental results demonstrate that the average transmission success rate of our system reaches almost 100%.
Abstract i
Table of Contents ii
List of Figures iii
Chapter 1 Introduction 1
Chapter 2 Problem Statement 6
2.1 Experiment Setups 7
2.2 Static Scenario 8
2.3 Moving Scenario 10
2.4 Design Goal 12
Chapter 3 Multi-Sink Placement 15
3.1 Geometric Model 16
3.2 Methodology 17
3.2.1 Related formulas 19
3.2.2 Multiple Sink Placement 21
Chapter 4 Implementation and Evaluation 25
4.1 Implementation 25
4.2 Evaluation 28
Chapter 5 Related Works 34
Chapter 6 Conclusion 35
Bibliography 36
Appendix A: The deriving relation of the line-of-sight region Lc 40
Appendix B: The error of an approximating ellipse 42
Appendix C: The mathematical model of sink placement problem 44
Appendix D: An enhance model by placing a sink on the roof 46
[1] Tseng, Y.-C, Wu, C.-H, Wu, F.-J, Huang, C.-F, King, C.-T, Lin, C.-Y, Sheu, J.-P, et al. A Wireless Human Motion Capturing System for Home Rehabilitation. In Proc. MDM 2009, IEEE Society (2009), to appear.
[2] Bowman, M., Debray, S. K., and Peterson, L. L. Investigating network architectures for body sensor networks.
[3] Ding, W. and Marchionini, G. Body Sensor Networks. Springer Publisher. 2006
[4] B. Braem, B. Latr´e, I. Moerman, C. Blondia, and P. Demeester. The Wireless Autonomous Spanning tree Protocol for multihop wireless body area networks. Mobile and Ubiquitous Systems: Networking & Services, 2006 Third Annual International Conference, July 2006, page(s): 1-8.
[5] Nordic Corp. Specification of Nordic nRF24L01.
http://www.nordicsemi.com/index.cfm?obj=product&act=display&pro=89
[6] Park, C. and Chou, P. -H. Eco, Ultra-Wearable and Expandable Wireless Sensor Platform. In Proc. BSN 2006, IEEE press (2006), 162--165.
[7] Fort, A., Desset, C., Wambacq, P., Biesen, L.V. Indoor body-area channel model for narrowband communications. Microwaves, Antennas & Propagation, IET, 1, 6 (2007) , 1197 – 1203
[8] Reusens, E., Joseph, W., Vermeeren, G., Kurup, D., Martens, L. Real human body measurements, model, and simulations of a 2.45 GHz wireless body area network communication channel. In Proc. ISSS-MDBS 2008, IEEE Press(2008), 149-152
[9] Xiao, B., Cao, J., Zhuge, Q., He, Y., Sha, E.H.-M. Approximation algorithms design for disk partial covering problem. In Proc. I-SPAN 2004, IEEE press (2004), 104 – 109
[10] Wang, Y.-C., Hu, C.-C., and Tseng, Y.-C., Efficient Placement and Dispatch of Sensors in a Wireless Sensor Network, IEEE transaction on Mobile Computing, 21,5(2008), 476-480,
[11] Chakrabarty, K., Iyengar, S.S., Hairong Qi and Eungchun Cho. Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks. IEEE Transactions on Computers, Vol. 51, No. 12. (December 2002), pp. 1448-1453.
[12] Frank Y. S. Lin and P. L. Chiu. A near-optimal sensor placement algorithm to achieve complete coverage-discrimination in sensor networks. IEEE Communications Letters, Vol. 9, No. 1, January 2005.
[13] Biovision BVH File Format
http://www.cs.wisc.edu/graphics/Courses/cs-838-1999/Jeff/BVH.html
[14] Hamel, M., Fontaine, R., and Boissy, P. In-Home Telerehabilitation for Geriatric Patients, IEEE Engineering in medicine and biology magazine. 27,4 (2008), 29-37.
[15] Choquette, S., Hamel, M. and Boissy, P. Accelerometer-based wireless body area network to estimate intensity of therapy in post-acute rehabilitation. Journal of NeuroEngineering and Rehabilitation, 5, 20(2008).
[16] Jovanov, E., Milenkovic, A., Otto, C. and Piet C de Groen. A wireless body area network of intelligent motion sensors for computer assisted physical rehabilitation. Journal of NeuroEngineering and Rehabilitation, 2, 6(2005) 1~10.
[17] Farella, E., Benini, L., Ricco, B. and Acquaviva, A. MOCA: A Low-Power, Low-Cost Motion Capture System Based on Integrated Accelerometers. Advances in Multimedia, 2007(2007),1-11
[18] Aylward, R., Paradiso,J. A. A Compact, High-Speed, Wearable Sensor Network for Biomotion Capture and Interactive Media. In Proc Sensys 2007, ACM press (2007), 380-390.
[19] Thomas Zasowski and Armin Wittneben. Performance of UWB Receivers with Partial CSI Using a Simple Body Area Network Channel Model. IEEE Journal on Selected Areas in Communications, Vol. 27, No. 1, January 2009.
[20] Keshmiri, Farshad; Fort, Andrew; Craeye, Christophe. Analysis of wave propagation for BAN applications. EuCAP 2009. 3rd European Conference on 23-27 March 2009, page(s):709 – 712.
[21] Zhoua, H. and Hub, H.. Human motion tracking for rehabilitation—A survey. Biomedical Signal Processing and Control, 3, 1(2008), 1-18
[22] Fitzgibbon, A. W., Pilu, M., and Fisher, R. B. Direct Least Square Fitting of Ellipses. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21,5 (1999), 476-480.
[23] Walter Gander, Gene H. Golub, and Rolf Strebel. Least-squares fitting of circles and ellipses. BIT 34 (1994), 558-578.
[24] R. Halíř and J. Flusser. Numerically stable direct least squares fitting of ellipses. WSCG'98 Conference Proceedings, 1998.
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