博碩士論文 955201108 詳細資訊




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姓名 林昆遠(Kun-Yuan Lin)  查詢紙本館藏   畢業系所 電機工程學系
論文名稱 ZigBee無線感測網路之跌倒偵測系統
(Fall Detection be ZigBee Wireless Sensor Network)
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摘要(中) 跌倒是高齡者和學步的孩童在居家生活中常見的意外。本系統是一套能偵測人體跌倒的無線感測網路(Wireless Sensor Network, WSN),主要目的在於有效地偵測跌倒以將其所造成的傷害減到最低。希望能在人體跌倒發生的最短時間內,發出警報給緊急聯絡人。偵測跌倒的方法是在地板上面佈置一個陣列的反射光感應器做為人體感測器,各感測器之間隔著固定距離,當人體壓在感測器上的時候,便會觸發被遮擋的感測器,藉由搭載群蜂(ZigBee)協定之節點以無線傳輸的方式,傳送訊號給網路協調器(Coordinator)及後端伺服器。伺服器分析這些感測器的位置及個數等等,來判斷是否為跌倒的發生。
摘要(英) Fall often happens to aged people and toddlers. It is a common accident during their daily lives. The purpose of our system was to effectively decrease the damage by detecting fall incidents and issuing an alerting message. Our system was based on wireless sensor network (WSN) conforming to the ZigBee protocol. We hoped that the system would inform someone as soon as possible when a fall happens. The detecting part consisted of reflective optical sensors deployed as an array on the ground. When a human body lay over the sensor array, the covered sensors would be triggered. The information would be transmitted to a coordinator of the WSN by ZigBee end devices connected to the covered sensors. The server of the WSN then received the information from the coordinator and analyzed the spatial pattern of the covered sensors to determine if a fall happened. Aimed to be used in bathrooms, we implemented the WSN fall detection system on an acrylic slab about the size of a bathroom floor area. Simulations were conducted to analyze the covered sensors’ pattern when a human body fell on the floor with different poses. Based on these data, a fall detection algorithm was developed.
關鍵字(中) ★ ZigBee
★ 無線感測網路
★ 跌倒
關鍵字(英) ★ ZigBee
★ Fall
★ WSN
★ Wireless Sensor Network
論文目次 中文摘要 I
英文摘要 II
誌謝 III
目錄 IV
圖目錄 VII
表目錄XI
Chapter 1 Introduction1
Chapter 2 Methods 3
Chapter 3 Results and Discussion 7
Chapter 4 Conclusion 9
Chapter 5 Figures 10
Chapter 6 References 18
Appendix A : 無線感測網路及ZigBee簡介 20
A-1無線感測網路20
A-2 ZigBee簡介 20
Appendix B : ZigBee套件及其硬體 23
B-1 Evaluation Board(EB板) 23
B-2 Development Board(DB板) 25
B-3 Battery Board(BB板) 27
Appendix C : ZigBee軟體架構 29
C-1 IAR軟體使用 29
C-1-1 程式燒錄流程 29
C-1-2 程式模擬流程 32
C-1-3 其他35
C-2 主要檔案及重要參數 35
C-2-1 主要檔案 35
C-2-2 重要參數 36
C-3 常用到的副程式37
C-4 GenericApp範例程式執行流程說明40
C-4-1 傳送40
C-4-2 接收41
C-5 HomeLighting範例程式執行流程說明42
C-5-1 傳送42
C-5-2 接收43
Appendix D : 感測地板46
D-1 Reflective Optical Sensor (CNY70)46
D-2 Sensor應用電路與ZigBee BB板結合 47
D-3 感測地板實體與配置 51
D-4 程式流程與GUI界面設定 53
D-4-1 近物感測器-SRC03391的傳送與接收(BB板)及SLC03394的傳送與接收(EB板)53
D-4-2 阻抗式晶片感測器-SRC03391的傳送 (BB板)及SLC03394的接收(EB板)58
D-4-3 GUI界面設定 59
D-4-3-1 近物感測器 59
D-4-3-2 阻抗式晶片感測器 62
D-4-4 注意事項 62
參考文獻 [1] Centers for Disease Control and Prevention. Web-based Injury Statistics Query and Reporting System (WISQARS). National Center for Injury Prevention and Control, Centers for Disease Control and Prevention (producer). Available at http://www.cdc.gov/ncipc/wisqars (accessed July 2008).
[2] J. A. Stevens, P. S. Corso, E. A. Finkelstein, T. R. Miller, The costs of fatal and nonfatal falls among older adults, Injury Prevention, VOL. 12, (2006), 290-295.
[3] C.-F. Juang, C.-M. Chang, Human Body Posture Classification by a Neural Fuzzy Network and Home Care System Application, IEEE Transactions On Systems, Man, and Cybernetics – Part A: Systems and Humans, VOL. 37, NO. 6, (November 2007), 984-994
[4] H. Na, S. F. Qin, D. Wright, A Smart Vision Sensor for Detecting Risk Factors of a Toddler's Fall, Proceedings of the 2007 IEEE International Conference on Networking, Sensing and Control, London, UK, 15-17 April 2007.
[5] H. Nait-Charif, S. J. McKenna, Activity Summarisation and Fall Detection in a Supportive Home Environment, Proceedings of the 17th International Conference on Pattern Recognition, 2004.
[6] C.-W. Lin, Z.-H. Ling, Yeng-Cheng Chang, Compressed-domain Fall Incident Detection for Intelligent Homecare, Journal of VLSI Signal Processing, VOL. 49, (2007), 393–408
[7] J. Chen, K. Kwong, D. Chang, J. Luk, R. Bajcsy, Wearable Sensors for Reliable Fall Detection, Proceedings of the 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference Shanghai, China, September 1-4, 2005.
[8] G. Wu, S. Xue, Portable Preimpact Fall Detector With Inertial Sensors, IEEE Transactions On Neural Systems And Rehabilitation Engineering, VOL. 16, NO. 2, (APRIL 2008), 178-183
[9] Y. Lee, J. Kim, M. Son, M. Lee, Implementation of Accelerometer Sensor Module and Fall Detection Monitoring System based on Wireless Sensor Network, Proceedings of the 29th Annual International Conference of the IEEE EMBS Cité Internationale, Lyon, France August 23-26, 2007.
[10] U. Lindemann, A. Hock, M. Stuber, W. Keck, C. Becker, Evaluation of a fall detector based on accelerometers: a pilot study, Med. Biol. Eng. Comput. , VOL 43, (2005), 548-551
[11] A. Sixsmith, N. Johnson, A Smart Sensor To Detect The Falls Of The Elderly, IEEE Pervasive Computing, Volume 03, Issue 2, (2004), pages:42- 47.
[12] N. Noury, A. Fleury, P. Rumeau, A.K. Bourke, G.Ó Laighin, V. Rialle, J.E. Lundy, Fall Dection – Principles and Methods, Proceedings of the IEEE EMBS Cité Internationale, Lyon, France, August 23-26, 2007.
[13] R. E. Saad, A. Bonen, K. C. Smith, B. Benhabib, Proximity Sensing for Robotics, in The Measurement, Instrumentation and Sensors Handbook, John G. Webster, ed., Boca Raton, Fla. : CRC Press, 1998.
指導教授 蔡章仁(Jang-Zern Tsai) 審核日期 2008-7-22
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