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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 黃寶儀(Polly Huang) | |
dc.contributor.author | Yi Jiing Song | en |
dc.contributor.author | 宋宜璟 | zh_TW |
dc.date.accessioned | 2021-06-15T05:43:56Z | - |
dc.date.available | 2011-08-20 | |
dc.date.copyright | 2010-08-20 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-08-19 | |
dc.identifier.citation | [1] ” Relative location estimation in wireless sensor networks”
N. Patwari, I. Hero, A.O., M. Perkins, N. Correal, and R. O’Dea.. Signal Processing, IEEE Transactions on [see also Acoustics, Speech, and Signal Processing, IEEE Transactions on], 51(8):2137–2148, Aug. 2003. [2] “Ad hoc positioning system (aps) using aoa”. D. Niculescu and B. Nath. INFOCOM 2003. Twenty-Second Annual Joint Conference of the IEEE Computer and Communications Societies. IEEE, 3:1734–1743 vol.3, 30 March-3 April 2003. [3] “The lighthouse location system for smart dust” K. R¨omer.. In Proceedings of MobiSys ’03, pages 15–30, New York, NY, USA, 2003. ACM. [4] “Range-free localization schemes for large scale sensor networks” T. He, C. Huang, B. M. Blum, J. A. Stankovic, and T. Abdelzaher. In Proceedings of MobiCom ’03, pages 81–95, New York, NY, USA, 2003. ACM. [5] “MoteTrack: A Robust, Decentralized Approach to RF-Based Location Tracking” Konrad Lorincz and Matt Welsh, Harvard University Division of Engineering and Applied Sciences Cambridge MA 02138, USA [6] ”RADAR: An in building RF-based user location and tracking system” P. Bahl and V. Padmanabhan. IEEE INFOCOM 2001, April 2001. [7]” DESIGN OF INDOOR POSITIONING SYSTEMS BASED ON LOCATION FINGERPRINTING TECHNIQUE” Kamol Kaemarungsi, UNIVERSITY OF PITTSBURGH SCHOOL OF INFORMATION SCIENCE, 2005 [8] “Deployment, Calibration, and Measurement Factors for Position Errors in 802.11-Based Indoor Positioning Systems” Thomas King, Thomas Haenselmann, and Wolfgang Effelsberg, Department for Mathematics and Computer Science University of Mannheim Germany, LoCA 2007 [9]” INDOOR LOCALIZATION SYSTEM USING RSSI MEASUREMENT OF WIRELESS SENSOR NETWORK BASED ON ZIGBEE STANDARD” Masashi Sugano, Tomonori Kawazoe, Yoshikazu Ohta, and Masayuki Muratab WSN2006, Banff (Canada), July 2006 [10] “A Microscopic Examination of an RSSI-Signature-Based Indoor Localization System” Tsung-Han Lina, I-Hei Nga, Seng-Yong Laua, Kuang-Ming Chenb, Polly Huang HotEmNets’08, June 2–3, 2008, Charlottesville, Virginia, USA [11]” A Bayesian sampling approach to indoor localization of wireless devices using RSSI” Vinay Seshadri, Manfred Huber, Gergely Zaruba, The University of Texas at Arlington,PerCom2005 [12]” Bayesian filters for location estimation” Dieter Fox, Jeffrey Hightower, Lin Liao,Dirk Schulz, Gaetano Borriello, Intel Research Seattle, Seattle, WA,PerCom2003 [13]” Monte Carlo Localization in Dense Multipath Environments Using UWB Ranging” Damien B. Jourdan, John J. Deyst, Jr., Moe Z. Win, Nicholas Roy, Massachusetts Institute of Technology, Proceedings of the IEEE Conference on UWB2005, Zurich, Switzerland. [14]” OTMCL: Orientation Tracking-based Monte Carlo Localization for Mobile Sensor Networks”, Marcelo H. T. Martins, Hongyang Chen, Kaoru Sezaki, Institute of Industrial Science the University of Tokyo [15]” Robot Localization Using Relative and Absolute Position Estimates” Puneet Goel, Stergios I. Roumeliotis and Gaurav S. Sukhatme, Department of Computer Science Institute for Robotics and Intelligent Systems University of Southern California [16]” An Accurate Localization for Mobile Robot Using Extended Kalman Filter and Sensor Fusion”, Jungmin Kim, Yountae Kim, and Sungshin Kim, 2008 International Joint Conference on Neural Networks (IJCNN 2008) [17]” Pedestrian Localisation for Indoor Environments” OliverWoodman, Robert Harle UbiComp’08, September 21-24, 2008, Seoul, Korea. [18]” A Wireless Sensor Network for Real-time Indoor Localization and Motion Monitoring” Lasse Klingbeil, TimWark IPSN 2008 St. Louis, Missouri, USA [19]” MOBILE ROBOT LOCALIZATION VIA FUSION OF ULTRASONIC AND INERTIAL SENSOR DATA”, E. Fabrizi, G. Oriolo, S. Panzieri, G. Ulivi, 8th International Symposium on Robotics with Applications [20]”COMPASS: A Probabilistic Indoor Positioning System Based on 802.11 and Digital Compasses”, Thomas King, Stephan Kopf, Thomas Haenselmann, Christian Lubberger, and Wolfgang Effelsberg WiNTECH’06, September 29, 2006, Los Angeles, California, USA. [21]” Pedestrian Tracking with Shoe-Mounted Inertial Sensors”, Eric Foxlin InterSense [22] ” Heuristic reduction of gyro drift for personnel tracking systems” Johann borestein, lauro ojeda, and surat kwanmuang THE JOURNAL OF NAVIGATION (2009), 62, 41–58. [23] ”Beyond the Kalman Filter : Particle Filter for Tracking Applications” Branko Ristic ,Sanjeev Arulampalam ,Neil Gordon [24] “Magnetic Diffusion: Disseminating Mission Critical Data for Dynamic Sensor Networks” Hsing-Jung Huang, Ting-Hao Chang, Shu-Yu Hu, Polly Huang MSWiM’05, October 10–13, 2005, Montreal, Quebec, Canada. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/46952 | - |
dc.description.abstract | 由於台灣已經步入了高齡化的社會,對於老年人的居住跟照護的需求也在增加當中,因此年長者的照顧也就更值得被重視。而在相關的研究當中,安養中心裡的年長者長期生活型態確很少被探索及調查,為了瞭解他們每天的生活,我們實際在北護護理之家佈建了以RSSI(接收信號強度)特徵為基礎的定位系統來追蹤年長者的定位資訊。
我們的定位系統是採用指紋特徵的概念,主要原理是將從tag收到的RSSI向量跟事前收集的RSSI值做比對藉以推測位置。然而無線訊號會被人體阻檔,因此這類的定位系統準確度降低的主要原因是在training(訓練) 階段與tracking(追蹤)階段方向不一致所造成的。我們提出了方向感知的定位系統來改善此問題,利用陀螺儀感測器來偵測使用者的方向,只有相同方向的RSSI 特徵才會被選出來做比對進而算出定位估測值。實驗結果顯示有考慮方向的系統大幅改善了定位準確度。 另一方面,我們收集了長期的定位資訊來分析年長者的生活起居的形態,藉由這些資訊,我們發現年長者對於空間使用的多元性不足,因為他們在一天當中花了大部份的時間在房間及交誼廳。 | zh_TW |
dc.description.abstract | Since Taiwan has become the aging society, the demand of living and nursing for senior citizen is increasing. Hence, it is necessary to pay attention on this research area. We are interested in the long term life behavior of the senior residents in the nursing home because it is rarely explored and investigated. To observe their life behaviors, RSSI signature based localization system has been deployed on the 5th floor of senior care center in Taiwan University Hospital Beihu Branch (abbreviated in “Beihu senior care center”) to provide their location traces.
Our localization system is a fingerprint type system. It maps between the RSSI vector received the tag and RSSI signatures pre-collected on the localization area to infer the location of tag. Due to RSSI may be blocked by the human body, the major factor for the accuracy degradation is due to the orientation inconsistence of the training phase and tracking phase. So, in order to get more accurate location estimations, we propose the orientation-aware localization. It uses the gyro to detect the heading information and only RSSI signatures having identical orientation are selected to find the possible location. The experiment result shows that there is a significant improvement after considering the heading information. On the other hand, we collect long term seniors’ location traces to analyze their life behaviors. We find out that they spent much of the time to stay in the bedroom as well as the living space so the diversity of their space usage is low. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T05:43:56Z (GMT). No. of bitstreams: 1 ntu-99-R97942104-1.pdf: 2526940 bytes, checksum: 82ae0899a08ff3ceb88f88ad716430e7 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | 誌謝 i
摘要 ii Abstract iv Contents vi List of Figures ix Chapter 1 Introduction 1 Chapter 2 Related Work 4 Chapter 3 Localization System 10 3.1 RSSI Fingerprint & KNN Estimation 10 3.2 Particle Filter 11 Chapter 4 The Importance of Orientation-Awareness 14 Chapter 5 Sensor-Assisted Orientation-Aware Localization 16 5.1 Heading Information Detection 16 5.2 The Number of Direction 19 5.3 System Architecture 19 Chapter 6 Implementation 21 6.1 Mobile Module Hardware 21 6.2 Angle Integration 22 6.3 Signal Processing 23 6.4 Min-Max Method 27 Chapter 7 Evaluation 30 7.1 Testbed 30 7.2 Training Data Collection 32 7.3 Experiment Setting 33 7.4 Results 34 Chapter 8 Long Term Monitoring 37 8.1 Data Processing 38 8.2 Space Partition in Biehu Senior care center 38 8.3 Ratio of Time Spent in Each Area 40 8.4 Number of Residents in Each Space 42 8.4.1 Bedroom 43 8.4.2 Recreation Room 1 43 8.4.3 Recreation Room 2 44 8.5 Usage of Space for Each Senior Resident 45 8.5.1 Bedroom 45 8.5.2 Recreation Room 1 46 8.5.3 Recreation Room 2 47 8.5.4 Corridor 47 8.5.5 Outside 48 8.6 Summary 48 Chapter 9 Conclusion &Future Work 50 Reference 53 | |
dc.language.iso | en | |
dc.title | 方向感知定位系統與長期照護 | zh_TW |
dc.title | Orientation-Aware Localization System for Long Term Elder Care | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 金仲達(Chung-Ta King),曾煜棋(Yu-Chee Tseng),朱浩華(Hao-Hua (Hao),陳恆順(Heng-Shuen Chen) | |
dc.subject.keyword | 陀螺儀,方向感知,RSSI特徵,指紋特徵, | zh_TW |
dc.subject.keyword | Gyro,orientation-aware,RSSI signature,fingerprint, | en |
dc.relation.page | 61 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2010-08-20 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 電信工程學研究所 | zh_TW |
顯示於系所單位: | 電信工程學研究所 |
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