(3.238.130.97) 您好!臺灣時間:2021/05/14 17:41
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果

詳目顯示:::

: 
twitterline
研究生:謝宏徽
研究生(外文):Hung-Hui Hsieh
論文名稱:基於類小腦神經網路控制器於跌倒偵測之居家照護系統設計與應用
論文名稱(外文):The Design and Application of CMAC-based Fall Detection in Home Care System
指導教授:白能勝白能勝引用關係
指導教授(外文):Neng-Sheng Pai
學位類別:碩士
校院名稱:國立勤益科技大學
系所名稱:電機工程系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2013
畢業學年度:101
語文別:中文
論文頁數:83
中文關鍵詞:三軸加速度計類小腦模式神經網路控制器跌倒偵測全向移動機器人Zigbee
外文關鍵詞:3-Axis AccelerometerCerebellar Model Articulation Controller (CMAC)Fall DetectionOmni-Directional Mobile RobotZigbee
相關次數:
  • 被引用被引用:0
  • 點閱點閱:266
  • 評分評分:
  • 下載下載:17
  • 收藏至我的研究室書目清單書目收藏:0
本論文旨在開發一基於類小腦神經網路於跌倒偵測之居家照護系統並應用於智慧型全向移動機器人。當老年人在家中活動或復健病患在醫院復健時可跟隨在其後,具有自動避障與跟隨使用者的功能,可以有效減少在室內移動時容易產生的碰撞問題,若使用者發生跌倒意外,可以即時偵測並以無線通報系統自動發出求救訊息,以及定時提醒用藥、手動發出緊急訊息等功能。
機器人系統結構使用PIC微控制器做為控制核心,周邊設備包含馬達編碼器、超音波距離感測器、全向輪(Omni-Directional Wheels)、三軸加速度計(3-Axis Accelerometer)、Zigbee、嵌入式GSM子板等。跌倒偵測系統(Fall Detection System)採用穿戴式感測器的偵測方式,使用者身上攜帶嵌入三軸加速度計的設備,經由Zigbee將感測器數據傳送至機器人電腦端,並使用類小腦模式神經網路控制器(Cerebellar Model Articulation Controller, CMAC)作為辨識演算法分析數據,判斷人體姿態。無線通報系統使用嵌入式GSM子板,在意外發生時可透過GSM網路發出SMS訊息給預先設定的號碼。
當使用者發生意外時如身體失去平衡或是失去意識跌倒,系統能即時偵測,並可使用無線通報系統,發出簡訊通知家人或是相關醫療照護單位,即時給予使用者救助,將意外傷害降至最低。

This paper aims to develop a cerebellar model articulation controller (CMAC)-based fall detection in home care system and apply in smart Omni-directional mobile robot. When elderly at home or the rehabilitation patients in at hospital rehabilitation, the proposed robot can follow the user with the function of Obstacle-Avoidance. In this way, it can effectively reduce the problem of indoor collisions. In the case of accidents, such as unconscious fall or fall out of balance, the robot can detect in real time for identification and send emergency message automatically. The system also have medicine controller and manually send messages functions.
The robot system uses the PIC microcontroller as the control core, and the peripheral equipment include industrial computer, ultrasonic distance sensor, omni-directional wheels, 3-axis accelerometer, Zigbee, embedded GSM board etc. Fall detection method use portable sensor-based recognition. The zigbee transmission module sends the sensor's data to the robot computer which uses CMAC as the recognition algorithm for posture recognition. The wireless communications system use embedded GSM board, it can send SMS message through GSM network to the pre-set number.
When accident happens, the system can detect in real time for identification and send emergency message to family or hospital automatically. Thus shortening the time for rescue after the accident, and reduce the damage from accident.

摘要 I
Abstract III
致謝 V
圖目錄 IX
表目錄 XII
第一章 緒論 1
1.1 研究動機與目的 1
1.2 文獻回顧 2
1.3 論文架構 4
第二章 系統架構與硬體設計 6
2.1 系統架構 6
2.2 硬體元件介紹 8
2.2.1 工業電腦 SBC86850 8
2.2.2 跌倒偵測系統適合之感測器簡介 10
2.2.3 Zigbee無線通訊設備 13
2.2.4 GSM通訊子板 16
2.2.5 系統流程圖 18
第三章 人體姿態分析 20
3.1 常見人體姿態 20
3.2 常見跌倒姿態 21
3.3 跌倒所產生的傷害與場所 22
3.4 跌倒發生的事前預防與事後處理 23
第四章 跌倒偵測方法與系統設計 27
4.1 跌倒偵測的方法 27
4.2 跌倒偵測系統設計 30
4.2.1. 類小腦模式神經網路 30
4.2.2. 跌倒偵測系統 42
4.2.3. 感測器相關設置 45
4.3 無線自動通報系統 47
4.4 定時提醒用藥系統 49
第五章 實驗成果 50
5.1 電腦端控制介面 52
5.2 跌倒偵測系統實驗 54
5.3 無線通報系統實驗 62
5.4 定時提醒用藥系統實驗 64
第六章 結論與未來展望 65
6.1 結論 65
6.2 未來展望 66
參考文獻 67


[1] World Population Ageing 1950-2050,http://goo.gl/vrr6t
[2] V. Bianchi, et al., "Fall Detection and Gait Analysis in a Smart-Home Environment", Gerontechnology Volume: 7, Issue: 2, pp. 73-73, 2008.
[3] S. G. Miaou, P. H. Sung, and C. Y. Huang, "A Customized Human Fall Detection System Using Omni-Camera Images and Personal Information" Proceedings of the 1st Distributed Diagnosis and Home Healthcare (D2H2) Conference Arlington, pp. 39-42, Virginia, USA, April 2-4, 2006.
[4] E. E. Stone, et al., "Extracting Footfalls from Voxel Data" 32nd Annual International Conference of the IEEE EMBS Buenos Aires, Argentina, August 31 - September 4, pp. 1119-1122, 2010.
[5] Y. Zigel, D. Litvak, and I. Gannot "A Method for Automatic Fall Detection of Elderly People Using Floor Vibrations and Sound—Proof of Concept on Human Mimicking Doll Falls" Biomedical Engineering, IEEE Transactions on, Volume 56 Issue 12, pp. 2858-2867, December, 2009.
[6] A.K. Bourke, J.V. O’Brien, and G.M. Lyons, "Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm" Gait &; Posture, Volume 26, pp. 194-199, 2007.
[7] C.F. Lai, S.Y. Chang, H.C. Chao, and Y.M. Huang, "Detection of Cognitive Injured Body Region Using Multiple Triaxial Accelerometers for Elderly Falling" Sensors Journal IEEE, Volume 11 Issue 3, pp. 763-770, 2011.
[8] V.D. Poorani, K. Ganapathy, and V. Vaidehi, "Sensor based decision making inference system for remote health monitoring " Recent Trends In Information Technology (ICRTIT) 2012 International Conference on, pp. 337-342, 2012.
[9] Y.W. Bai, S.C. Wu, and C.L. Tsai, "Design and implementation of a fall monitor system by using a 3-axis accelerometer in a smart phone" Consumer Electronics, IEEE Transactions on, pp. 1269-1275, 2012.
[10] A.M. Tabar, A. Keshavarz, and H. Aghajan, "Smart home care network using sensor fusion and distributed vision-based reasoning" VSSN '06Proceedings of the 4th ACM international workshop on Video surveillance and sensor networks, pp. 145-154, 2006.
[11] Leone1, et al., "A multi-sensor approach for People Fall Detection in home environment" Author manuscript, published in "Workshop on Multi-camera and Multi-modal Sensor Fusion Algorithms and Applications M2SFA2, pp. 1-12, 2008.
[12] J. S. Albus, "Data storage in the cerebellar model articulation controller (CMAC)" J. Dyn. Syst., Meas., Contr., Trans. ASME, vol. 97, pp. 228-233, 1975.
[13] C.P. Hung, M.H. Wang, C.H. Cheng, and W.L. Lin, "Fault diagnosis of steam turbine-generator using CMAC neural network approach" Neural Networks, 2003. Proceedings of the International Joint Conference on, Volume 4, pp. 2988-2993, 2003.
[14] W.S. Lin, C.P. Hung, and M.H. Wang, "CMAC-based fault diagnosis of power transformers" Neural Networks, 2002. IJCNN '02. Proceedings of the 2002 International Joint Conference on, Volume 1, pp. 986-991, 2002.
[15] 艾訊科技 SBC86850 技術手冊,http://goo.gl/fq1W5
[16] 惠普特企業股份有限公司,http://goo.gl/3MMxc
[17] ATmega128L DataSheet,http://goo.gl/m12nl
[18] CC2420 DataSheet ,http://goo.gl/SV8R8
[19] 豐樂電子(香港)有限公司,http://goo.gl/9KbxB
[20] 台灣老年學暨老年醫學會會訊第51期,http://goo.gl/PRxgU
[21] 美國老年醫學會雜誌, “J Am Geriatr Soc” Volume 49, pp.664-672, 2001.
[22] Federal Trade Commission,http://goo.gl/8Vg6W
[23] Dartmouth Conferences,http://goo.gl/WmN3M
[24] 黃聖富,全向移動自主性跟隨機器人之設計與實現,碩士論文,電機工程所,國立勤益科技大學,2010。
[25] 賴俊源,基於HMM之語音辨識和RFID技術實現機器人之導覽系統,碩士論文,電機工程所,國立勤益科技大學,2011。
[26] P. Udsatid, N. Niparnan, and A. Sudsang, "Human Position Tracking for Side By Side Walking Mobile Robot using Foot Positions" Robotics and Biomimetics (ROBIO), pp. 1374-1378, Guangzhou, December 11-14, 2012.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top