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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 陳中平(Chung-Ping Chen) | |
dc.contributor.author | Chia-Hsiang Lin | en |
dc.contributor.author | 林家祥 | zh_TW |
dc.date.accessioned | 2021-06-07T17:48:14Z | - |
dc.date.copyright | 2013-06-21 | |
dc.date.issued | 2012 | |
dc.date.submitted | 2013-02-23 | |
dc.identifier.citation | [1] M.J. Mathie, N.H. Lovell, A.C.F. Coster, and B.G. Celler “Determining Activity Using a Triaxial Accelerometer,” Engineering in Medicine and Biology, 2002. 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, 2002.
[2] D.M. Karantonis, M.R. Narayanan, M.J. Mathie, N.H. Lovell, and B.G. Celler “Implementation of a Real-Time Human Movement Classifier Using a Triaxial Accelerometer for Ambulatory Monitoring,” Information Technology in Biomedicine,IEEE Transactions on Vol.10, Issue.1, 2006. [3] A. K. Bourke, J. V. O’Brien, and G. M. Lyons “Evaluation of a Threshold-Based Tri-Axial Accelerometer Fall Detection Algorithm,” Gait & Posture, 26, pp.194-199, 2007. [4] Chia-Chi Wang “Development of a Fall Detecting System for the Elderly Residents,” The 2nd International Conference on 16-18 May 2008 , pp.1359-1362, Digital Object Identifier 10.1109/ICBBE, 2008. [5] Lindemann U, Hock A, Stuber M, Keck W, and Becker C “Evaluation of a Fall Detector Based on Accelerometers: a Pilot Study,”Med. Biol. Eng.Comput, 2005. [6] A. Y. Jeon, I. C. Kim, J. H. Jung, S. Y. Ye, J. H. Kim, K. G. Nam, S. W. Baik, J. H. Ro, and G. R. Jeon,“Implementation of the Personal Emergency Response System using a 3-axial Accelerometer,” in information Technology Application in Biomedicine, 2007. ITAB 2007. 6th International Special Topic Conference, 2009, pp. 223-226. [7] M. Kangas, A. Konttia, P. Lindgren, I. Winblad, T. Jamsa, “Comparison of low-complexity fall detection algorithms for body attached accelerometers,”Gait & Posture, vol. 28 , pp. 285-291, 2008. [8] D. Karantonis, M. Narayanan, M. Mathie, N. Lovell, and B. Celler, “Implementation Of a real-time human movement classifier using a Triaxial accelerometer for ambulatory monitoring,” IEEE Trans. Inf. Technol. Biomed., vol. 10, pp. 156–167, 2006. [9] M. N. Nyan, F. E. H. Tay, A. W. Y. Tan, and K. H. W. Seah“Distinguishing Fall Activities from Normal Activities by Angular Rate Characteristics and High-Speed Camera Characterization,” Medical Engineering and Physics, 28(8), pp.842-849, 2006. [10] Tong Zhang, Jue Wang, Ping Liu, and Jing Hou “Fall Detection by Embedding an Accelerometer in Cellphone and Using KFD Algorithm,” IJCSNS International Journal of Computer Science and Network Security, Vol.6 No.10, Oct, 2006. [11] Jiangpeng Dai, Xiaole Bai, Zhimin Yang, Zhaohui Shen, and Dong Xuan“PerFallD: APervasive Fall Detection System Using Mobile Phones,” Pervasive Computing and Communications Workshops (PERCOM Workshops), on Digital Object Identifier 8th IEEE International Conference, 2010. [12] 許宏駿,「以個人數位助理(PDA)為基礎之可穿戴式跌倒即時監測系統」,逢甲大學自動控制研究所碩士論文,2004。 [13] 王致中,「開發一利用慣性感測器與肌電訊號分辨日常生活與跌倒的偵測系統」,交通大學機械工程研究所碩士論文,2009。 [14] C. Lai, Y. Huang, H. Chao, and J. Park“Adaptive Body Posture Analysis Using Collaborative Multi-Sensors for Elderly Falling Detection,” Intelligent Systems, IEEE, 2010. [15] Thomas Degen, Heinz Jaeckel, Michael Rufer, and Stefan Wyss“Speedy:a Fall Detector in a Wrist Watch”, Proceedings. Seventh IEEE International Symposium on Wearable Computers, 2005. [16] Rhee S, Yang BH, Asada HH. Associate Member, Artifact-Resistant Power-Efficient Design of Finger-Ring Plethysmographic Sensor. IEEE Trans Biomed Eng. 2001 Jul;48(7):195-805. [17] Sola J, Castoldi S, Chetelat O, Correvon M, Dasen S, Droz S, Jacob N, Kormann R, Neumann V, Perrenoud A, Pilloud P, Verjus C, Viardot G, SpO2 Sensor Embedded in a Finger Ring: design and implementation. Conf Proc IEEE Eng Med Biol Soc. 2006;1:4295-4298. [18] Medelson Y and Ochs BD, Noninvasive Pulse Oximetry Utilizing Skin Reflectance Photoplethysmography, IEEE Trans Biomed Eng. 1998, Oct;35(10)798-805. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/15582 | - |
dc.description.abstract | 跌倒及跌倒以致傷殘的發生機率在老年人中非常的普遍。在台灣,每年約13.6%之老年人曾經因為跌倒而導致骨折。通常那些因跌倒而骨折的老年人無法自行爬起就醫,而需要其他人幫忙及照護。跌到偵測系統的發展正是為了回應老年看護與通報的殷切需求。過去通常使用微機電(MEMS)系統提供老年人的跌倒偵測系統,然而這套系統卻要即時偵測真實跌倒情況,卻有其難度。在這篇論文中,我們提出一種演算法,透過使用簡單便宜的三軸加速儀和陀螺儀作為感測器,和Arduino以及手機做結合,用以估測人體姿態。藉由將裝置微小化並且放置於手腕上,以及經由藍芽將資料傳輸至手機上,可獲得老年人身體姿勢,可用以偵測可能跌倒的警訊
本系統跌倒偵測的平均敏感度為97%,平均變異度為99.111%,判斷跌倒偵測所取的測量點數為30點決策一次。 因為傳統型血壓計和血氧機體積大且不容易攜帶,我們利用MEMS將兩者皆微小化於手腕錶帶上,以提升準確度,使其能夠和傳統型的偵測器抗衡。我們發展出的偵測器能將所得到的數據傳到手機上,再藉由手機送回控管中心,使病患的生理特徵能夠被及時掌握,不但可提升危機時將病患救回的機率,更能防患於未然。 | zh_TW |
dc.description.abstract | The occurrence of falls and fall-related injuries are very common. Each year in Taiwan, approximately 13.6% of the elder people experiences injuries and even suffers a fracture or sprain, when they fall. Unsurprisingly, once they fall and sustain a fracture, they cannot stand up on their own; instead, they need the immediate help offered by a third party, either a family member or a caretaker. It is with the aim to offer timely help to elderly people who experience accidental falls or slips that a fall detection system has been developed. However, the fall detection system developed in the past, which utilizes the micro-electromechanical systems (MEMS), does not provide real-time detection of a fall, thereby limiting its value and efficacy. In this thesis, we propose an algorithm, which makes use of cheap and simple accelerometer and gyroscope as sensors, and we also employ an Arduino and android phone to estimate the posture and positions of the human body at the moment of a fall. By miniaturizing our device so that it is wearable on the wrist, we design and create a fall detection system that can transmit data through a Bluetooth serial port to the android phone to determine the postures of the elder people who wear the device. In so doing, we can easily determine if a fall has occurred, and, if so, in what posture the elderly person falls or slips.
The average sensitivity of our fall detection system is 97% and the average specificity is 99.111%. We make our falling detection decision every 30 measurement point. Because the traditional sphygmomanometer and blood oxygen machine are too big and not easy to carry, we have decided to use MEMS to miniaturize these two devices into an integrated wristband. Besides, not only do we improve the accuracy of our device, but we also make it a smart device that can transmit the data we catch to an android phone which then immediately sends the relevant data to the medical control center for further analysis. By thus relaying the vital signs of the elderly person who wears the device to the control center, our detection system can not only provide the vital information needed to save an elderly when a fall occurs, but it can also help to plot preventive actions; that is, it may detect a fall even before it actually occurs. | en |
dc.description.provenance | Made available in DSpace on 2021-06-07T17:48:14Z (GMT). No. of bitstreams: 1 ntu-101-R98943145-1.pdf: 3209515 bytes, checksum: 93ad5ac3124ebf45d56f5d752b607c60 (MD5) Previous issue date: 2012 | en |
dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 摘要 iii ABSTRACT iv CONTENTS vi LIST OF FIGURES ix LIST OF TABLES xiii Chapter 1 Introduction 1 1.1 Introduction 1 1.2 Telecare System 2 1.3 Motivation 2 1.4 Objectives 3 1.5 Thesis Organization 4 Chapter 2 Recent Research 5 2.1 Types of Fall-detection system 6 2.1.1 Current Research on the Application of Tri-axial Acceleration Sensors on the Fall Detection System 8 2.1.2 The Gyroscope Fall Detection System 11 2.1.3 Phone Fall Detection System 12 2.1.4 Research on Multi-sensor Fall Detection System 13 2.2 Positions Sensors Are Placed 16 2.3 Piezoelectric Thin Film Sensor 17 2.3.1 Contact Sensor 19 2.3.2 Stethoscope 20 2.4 High-resolution analog-to-digital Converter 21 Chapter 3 Overview of Related Knowledge 22 3.1 Analysis of Falling positions 22 3.2 Introduction to System Hardware 23 3.2.1 Micro-controller 24 3.2.2 Introduction to Accelerometer ADXL345 27 3.2.3 Principle of Accelerometer 28 3.2.4 Specification of Accelerometer ADXL345 35 3.2.5 Introduction to Gyroscope 36 3.2.6 Principle to Gyroscope 38 3.2.7 Specification of Gyroscope ITG-3205 41 3.2.8 Bluetooth 43 3.3 Architecture of Pressure type Sphygmograph 44 3.3.1 Piezo film sensors module 45 3.4 Architecture of Oximeter 47 3.4.1 High-resolution Analog-to-digital converter 49 Chapter 4 Research Methods and Procedure 51 4.1 Fall Detection System 51 4.1.1 Architecture of Fall detection system 51 4.1.2 System Flow 52 4.1.3 Direction Cosine Matrix 54 4.1.4 Algorithms for implement DCM on accelerometer and gyroscope 68 4.1.5 Android Software Design 70 4.2 Numerical Analysis of Sphygmograph 70 4.3 Architecture of Oximeter 71 4.3.1 Importance of High-resolution Analog-to-Digital Converter 71 Chapter 5 Implementation and Experimental Results of Telecare System 73 5.1 The Telecare System and the Fall Detection System 73 5.1.1 Background of Experiment 73 5.1.2 Results 74 5.1.3 Experiment Data Analysis 82 5.2 The Sphygmography system 84 5.3 High-Resolution Analog-to-Digital Converter 85 Chapter 6 Conclusion and the Future 88 6.1 Conclusion 88 6.2 The Future 88 Bibliography 90 | |
dc.language.iso | en | |
dc.title | 嵌入式微機電系統在生醫電子的應用 | zh_TW |
dc.title | Embedded Micro Electro Mechanical system design in Bio-electronics and its Implementation | en |
dc.type | Thesis | |
dc.date.schoolyear | 101-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 陳少傑(Sao-Jie Chen),Feipei Lai(Feipei Lai) | |
dc.subject.keyword | 跌倒偵測,三軸加速度計,陀螺儀,壓電薄膜感應器,血氧計, | zh_TW |
dc.subject.keyword | Falling detection,accelerometer,gyroscope,Arduino,PVDF,oximeter, | en |
dc.relation.page | 92 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2013-02-23 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電子工程學研究所 | zh_TW |
顯示於系所單位: | 電子工程學研究所 |
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