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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 | 呂東武(Tung-Wu Lu) | |
dc.contributor.author | Yu-Chi Lin | en |
dc.contributor.author | 林郁埼 | zh_TW |
dc.date.accessioned | 2021-06-15T03:04:23Z | - |
dc.date.available | 2019-07-30 | |
dc.date.copyright | 2009-08-06 | |
dc.date.issued | 2009 | |
dc.date.submitted | 2009-07-30 | |
dc.identifier.citation | Aase, S. O. (2002). 'Compression depth estimation for CPR quality assessment using DSP on accelerometer signals.' IEEE Transactions on Biomedical Engineering 49(3): 263.
Boonstra, M. C. (2006). 'The accuracy of measuring the kinematics of rising from a chair with accelerometers and gyroscopes.' Journal of Biomechanics 39(2): 354. Dejnabadi, H. (2006). 'Estimation and visualization of sagittal kinematics of lower limbs orientation using body-fixed sensors.' IEEE Transactions on Biomedical Engineering 53(7): 1382. Enge, P. (1999). 'Special issue on global positioning system.' Proceedings of the IEEE 87(1): 3. Karantonis, D. M. (2006). 'Implementation of a real-time human movement classifier using a triaxial accelerometer for ambulatory monitoring.' IEEE Transactions on Information Technology in Biomedicine 10(1): 156. Kumahara, H. (2004). 'The use of uniaxial accelerometry for the assessment of physical-activity-related energy expenditure: a validation study against whole-body indirect calorimetry.' British Journal of Nutrition 91(2): 235. Levine, J. A. (2001). 'Validation of the Tracmor triaxial accelerometer system for walking.' Medicine and Science in Sports and Exercise 33(9): 1593. Luinge, H. J. (2004). 'Inclination measurement of human movement using a 3-D accelerometer with autocalibration.' IEEE Transactions on Neural Systems and Rehabilitation Engineering 12(1): 112. Luinge, H. J. (2007). 'Ambulatory measurement of arm orientation.' Journal of Biomechanics 40(1): 78. Mathie, M. J. (2003). 'Detection of daily physical activities using a triaxial accelerometer.' Medical, & Biological Engineering & Computing 41(3): 296. Mathie, M. J. (2004). 'Accelerometry: providing an integrated, practical method for long-term, ambulatory monitoring of human movement.' Physiological measurement 25(2): 1. Mayagoitia, R. E. (2002). 'Accelerometer and rate gyroscope measurement of kinematics: an inexpensive alternative to optical motion analysis systems.' Journal of Biomechanics 35(4): 537. Najafi, B. (2003). 'Ambulatory system for human motion analysis using a kinematic sensor: monitoring of daily physical activity in the elderly.' IEEE Transactions on Biomedical Engineering 50(6): 711. Nissanka, B. P., C. Anit, et al. (2000). The Cricket location-support system. Proceedings of the 6th annual international conference on Mobile computing and networking. Boston, Massachusetts, United States, ACM. Odonovan, K. J. (2007). 'An inertial and magnetic sensor based technique for joint angle measurement.' Journal of Biomechanics 40(12): 2604. Parkka, J. (2006). 'Activity classification using realistic data from wearable sensors.' IEEE Transactions on Information Technology in Biomedicine 10(1): 119. Schutz Y., Weinsier S., Terrier P. and Durrer D. (2002) A new accelerometric method to assess the daily walking practice. International Journal of Obesity. 26:111-118 Terrier, P. (2001). 'Can accelerometry accurately predict the energy cost of uphill/downhill walking?' Ergonomics 44(1): 48. Want, R. (1992). 'The active badge location system.' ACM Transactions on Information Systems 10(1): 91. Ward, A. (1997). 'A new location technique for the active office.' IEEE Personal Communications Magazine 4(5): 42. Wu, Y. (2004). 'A robust DSP integrator for accelerometer signals.' IEEE Transactions on Biomedical Engineering 51(2): 385. Wei M. and Schwarz K.P. (1990) A strapdown inertial algorithm using an earth-fixed Cartesian frame. Journal of The Institute of Navigation. 37(2):153-167 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/44548 | - |
dc.description.abstract | 行動裝置位置導向之真實情境展現界面,為一人性化的人機界面,但卻十分仰賴足夠精確之位置及姿態資訊。利用微型慣性定位技術可提供行動裝置所需的狀態資訊,並可在外部定位(如GPS、WiFi定位等)無法持續取得訊號的情況下,提供連續位置及姿態資訊,但卻有隨時間增加之累積誤差問題。
本研究旨在開發新型室內定位技術,透過人體運動特徵(human movement characteristics)建立人體於室內常見動作之人體運動數學模型。當長時間無法接收外部定位訊號,如處於騎樓或室內時,能藉由開發所得之人體運動數學模型,配合慣性定位系統所得之資料進行人體定位。本研究針對室內常見動作,包括步行、上下樓梯、坐到站以及站到坐,建立不同動作之人體運動數學模型,並透過小波轉換技術將以上動作進行判別與分類。於步行及上下樓梯之運動數學模型中,亦將轉彎列入考慮,讓此定位技術能夠更完善的運用於人體定位。 於基礎定位技術開發完成後,為求完整的定位效果與將來實用性考量,本研究將初步定位結果加入圖資,且於圖資化過程中加入座標系統,標示出定位位置,並於地圖下方顯示定位座標值,完成精確人體定位技術開發。 為測試此技術的發展性,本研究將感測器放置於人體不同位置,討論其定位效果,以及定位設備最小需求。除此之外,為驗證此技術之重複性和可靠度,透過六位不同受試者進行多項定位結果分析並進行討論。研究結果顯示,於六位不同受試者的實驗驗證下,平地走路的誤差率在3%以下,各種室內常見動作之判別成功率都在75%以上,而此技術感測器放置位置為胸骨劍突處及小腿處精準度最佳。 | zh_TW |
dc.description.abstract | Location-Based Life-Reality (LBLR) realized on mobile device is proved to be a user-friendly man-machine interface (MMI). However, it heavily relies on the fact that the system must continuously get accurate position and attitude information. Inertia positioning of micro sensors can continuously provide LBLR mobile device with needed state information. However, it has the problem that its integrator accumulates the sensor errors and its accuracy dramatically decays with time.
The purpose of the current study was developing the new indoor positioning technology which was founded on human mathematical model constructed with human movement characteristic during common in indoor movement. When the signal of external positioning system (EPS) is unavailable, such as people under overhang of a building or indoors, our new positioning technology which was utilized the combination of the developed human movement mathematical models and the inertial data can effectively position the human location. This study aimed to develop different movement of human movement mathematical model during common indoor movements, including level walking, stair ascent and descent, sit-to-stand and stand-to-sit, and to apply the wavelet transformation technology in reorganization and classification of all the movements. For the human mathematical model during level walking, stair ascent and descent movements, movement of turning would be also considered in the establishment of this positioning technology for more accurate human positioning. On the strength of the consideration of the positioning effects and practicability of our newly developed positioning technology, we added the map and the coordinate system in the mapping process for displaying the positioning result and the coordinates below the map shown in the monitor of the mobile positioning device. In order to test the practicability of this positioning technology, we put the inertial sensors on different body positions of human for confirming the positioning effects and the minimum requirements of this technique. Otherwise, six subjects were recruited to participate in different motion experiments for confirming the reproduction and validity of the new positioning technology. The results of different motion experiments in the current study showed that the positioning error during level walking was below 3%, the rate of success for classifying different indoor movements was greater than 75%, and the best sensor placements were Xphoid process and shank. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T03:04:23Z (GMT). No. of bitstreams: 1 ntu-98-R96548022-1.pdf: 3816608 bytes, checksum: 8b60e2dae291e13c9339622a2c5d39bc (MD5) Previous issue date: 2009 | en |
dc.description.tableofcontents | 摘要 i
Abstract ii 目錄 iv 圖目錄 vii 表目錄 x 第一章 緒論 1 第一節 研究背景 1 第二節 定位系統面臨的問題 1 第三節 慣性定位系統之應用 3 第四節 研究目的 4 第二章 基礎理論 5 第一節 慣性定位設備之誤差 5 一、 系統訊號誤差 5 二、 系統積分誤差 8 第二節 積分原理與方法 9 第三節 人體運動數學模型 11 第四節 步長向量(step vector) 16 第五節 小波轉換理論 17 第六節 數位影像處理 21 第三章 實驗材料與方法 23 第一節 實驗設備與受試者 23 一、 慣性定位儀 23 二、 受試者 24 第二節 研究方法 25 一、 人體運動之分析 25 二、 慣性資料之計算 26 三、 建構不同狀態下之人體運動數學模型 27 四、 將慣性感應器擺放於人體不同位置 28 五、 動作判別與分類 28 六、 連接所有動作與相對應之人體運動數學模型 29 七、 自行建立室內地圖 30 八、 移動路徑圖資化 31 第四章 實驗驗證設計 33 第一節 慣性感應器放置處 33 第二節 靜態校正 34 第三節 ㄇ字型直線路徑試驗 34 第四節 慣性感測器結合人體運動數學模型與直接積分之比較試驗 35 第五節 慣性感測器不同擺放位置對人體運動數學模型之影響探討 35 第六節 人體數學模動作判別與分類成功率試驗 36 第七節 人體數學模型所需之慣性資料最小需求探討 36 第八節 室內至室外之連續動作試驗 36 第九節 利用自製地圖進行實際測試 37 第十節 模型之重複性及可靠度探討 37 第五章 結果與討論 38 第一節 ㄇ字型直線路徑測試結果 38 第二節 慣性感測器結合人體運動數學模型與直接積分之比較試驗結果 39 一、 平地走路試驗 39 二、 上下樓梯試驗 41 三、 比較慣性定位系統結合人體運動數學模型及直接積分之定位結果 44 第三節 慣性感應器不同擺放位置對於定位結果之影響 46 第四節 建立人體數學模型所需之慣性資料最小需求 47 第五節 判別並分類步行、上下樓梯、坐下起立等不同運動狀態 48 第六節 室內至室外之連續動作試驗結果 54 第七節 利用自製地圖進行實際測試 57 一、 自製地圖 57 二、 座標系統 59 三、 國立台灣大學第一男研究生宿舍試驗結果 60 第八節 結論 63 第九節 未來方向 63 參考文獻 64 | |
dc.language.iso | zh-TW | |
dc.title | 以人體運動特徵為基礎之精確人員定位追蹤技術研究 | zh_TW |
dc.title | Accurate Human Positioning Based on Human Movement Characteristics | en |
dc.type | Thesis | |
dc.date.schoolyear | 97-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 林聰穎(Tsung-Ying Lin),陳祥和(Hsiang-Ho Cheni) | |
dc.subject.keyword | 慣性定位系統,人體運動數學模型,動作分類,圖資化,定位, | zh_TW |
dc.subject.keyword | IMUs,human model,movement classification,map,calibration,positioning, | en |
dc.relation.page | 65 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2009-07-30 | |
dc.contributor.author-college | 工學院 | zh_TW |
dc.contributor.author-dept | 醫學工程學研究所 | zh_TW |
顯示於系所單位: | 醫學工程學研究所 |
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