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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 許永真 | |
dc.contributor.author | Chao-Wei Pan | en |
dc.contributor.author | 潘昭瑋 | zh_TW |
dc.date.accessioned | 2021-06-12T18:19:30Z | - |
dc.date.available | 2007-09-03 | |
dc.date.copyright | 2007-09-03 | |
dc.date.issued | 2007 | |
dc.date.submitted | 2007-08-27 | |
dc.identifier.citation | [1] L. Bao and S. Intille. Activity recognition from user-annotated acceleration data. In
Proceedings of the 2nd International Conference on Pervasive Computing (Pervasive 2004), pages 1–17, 2004. [2] G. S. Chambers, S. Venkatesh, G. West, and H. Bui. Hierarchical recognition of intentional human gestures for sports video annotation. In Proceedings of the 16th International Conference on Pattern Recognition (ICPR 2002), pages 1082–1085, 2002. [3] G. S. Chambers, S. Venkatesh, G. West, and H. Bui. Segmentation of intentional human gestures for sports video annotation. In Proceedings of the 10th International Multimedia Modeling Conference (MMM 2004), pages 124–129, 2004. [4] E. Farella, M. O’Modhrain, L. Benini, and B. Ricc`o. Gesture signature for ambient intelligence applications: A feasibility study. In Proceedings of the 4th International Conference on Pervasive Computing (Pervasive 2006), pages 288–304, 2006. [5] E. A. Heinz, K. S. Kunze, M. Gruber, D. Bannach, and P. Lukowicz. Using wearable sensors for real-time recognition tasks in games of martial arts - an initial experiment. In Proceedings of the 2nd IEEE symposium on Computational Intelligence and Games (CIG 2006), pages 98–102, 2006. [6] N. Kern, G. Tr‥oster, B. Schiele, H. Junker, and P. Lukowicz. Wearable sensing to an-notate meeting recordings. In Proceedings of the 6th IEEE International Symposium on Wearable Computers (ISWC 2002), pages 186–196, 2002. [7] A. Krause, M. Ihmig, E. Rankin, D. Leong, S. Gupta, D. Siewiorek, A. Smailagic, M. Deisher, and U. Sengupta. Trading off prediction accuracy and power consumption for context-aware wearable computing. In Proceedings of the 9th IEEE International Symposium on Wearable Computers (ISWC 2005), pages 20–26, 2005. [8] K. Kunze, M. Barry, E. A. Heinz, P. Lukowicz, D. Majoe, and J. Gutknecht. Towards recognizing tai chi - an initial experiment. In Proceedings of the 3rd International Forum on Applied Wearable Computing, 2006. [9] S. Lee and K. Mase. Activity and location recognition using wearable sensors. Pervasive Computing, IEEE, 1(3):24–32, 2002. [10] J. M‥antyj‥arvi, J. Himberg, and T. Sepp‥anen. Recognizing human motion with multiple acceleration sensors. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, pages 747–752, 2001. [11] J. K. Perng, B. Fisher, S. Hollar, and K. S. J. Pister. Acceleration sensing glove (asg). In Proceedings of the 3th IEEE International Symposium on Wearable Computers (ISWC 1999), pages 178–180, 1999. [12] L. R. Rabiner. A tutorial on hidden markov models and selected applications in speech recognition. Proceedings of the IEEE, 77(2):257–286, 1989. [13] C. Randell and H. Muller. Context awareness by analysing accelerometer data. In Proceedings of the 4th IEEE International Symposium onWearable Computers (ISWC 2000), pages 175–176, 2000. [14] K. Van Laerhoven and O. Cakmakci. What shall we teach our pants? In Proceedings of the 4th IEEE International Symposium on Wearable Computers (ISWC 2000), pages 77–83, 2000. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/27767 | - |
dc.description.abstract | 乳癌患者在手術完畢後,必須要進行一系列的復健動作,然而病患在家中自行進行復健動作的過程中,常常因為疼痛,或種種因素而使得整個復健動作流程不完整或中斷。本研究透過動作辨識來紀錄病患每天在家中的復健情形將會對醫生在門診時能更快速的了解病人復健狀況,給予病人更有效的復健療程。
本篇論文主要採用人體在進行復健動作過程中的加速度作為訊號來源,利用機器學習(Machine Learning)的方法,針對八種基本的乳癌復健動作進行辨識。本文探討了加速度訊號的重要特徵值,並以隱藏式馬可夫模型(Hidden Markov Model)為理論基礎進行一系列的實驗來驗證其辨識的準確率。實驗結果指出,在使用平均值、能量、頻譜亂度、相關係數當特徵值時所得到的模型可達到98%的準確性。 | zh_TW |
dc.description.abstract | Rehabilitation exercises after breast cancer surgery can help
prevent post-operation complications. This thesis adopts activity recognition technique to identify and record patients' rehabilitative exercises. This information helps doctors monitor the patients' conditions in follow-up visits. This thesis presents a activity recognition system based on continuous hidden Markov models. Accelerometers are used to capture the upper body movements when patients do rehabilitation. Four different representative features, mean, energy, entropy, and correlation, are extracted from signals. The recognition rate of exercises is about 98%. The performance of the recognizer is also evaluated in both user dependent and user independent cases. | en |
dc.description.provenance | Made available in DSpace on 2021-06-12T18:19:30Z (GMT). No. of bitstreams: 1 ntu-96-R94922141-1.pdf: 1601711 bytes, checksum: 881ce6d9a4f30e242d54ec40ad470fb2 (MD5) Previous issue date: 2007 | en |
dc.description.tableofcontents | Acknowledgments ii
Abstract iii List of Figures viii Chapter 1 Introduction 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 Chapter 2 RelatedWork 5 2.1 Detection of Ambulatory Mode . . . . . . . . . . . . . . . . . . . . . 6 2.2 ADL Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2.3 Gesture Recognition . . . . . . . . . . . . . . . . . . . . . . . . . . 8 Chapter 3 Experiment Design 9 3.1 Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2 Accelerometers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 3.2.1 Hardware . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.2.2 Placement . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 3.3 Target Rehabilitation Exercises . . . . . . . . . . . . . . . . . . . . . 14 3.4 Proposed Solution . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.5 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.6 Recognition Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . 21 Chapter 4 Experiment and Evaluation 25 4.1 Data Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 4.2 Experiment Results . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 Chapter 5 Conclusion 33 Bibliography 35 Appendix A The Breast Cancer Rehabilitation Exercise 37 | |
dc.language.iso | en | |
dc.title | 基於加速度訊號之復健動作辨識 | zh_TW |
dc.title | Rehabilitation Exercises Recognition Based on Acceleration Signals | en |
dc.type | Thesis | |
dc.date.schoolyear | 95-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 黃寶儀,朱浩華 | |
dc.subject.keyword | 加速度,動作辨識,復健, | zh_TW |
dc.subject.keyword | Acceleration,Activity Recognition,Rehabilitation, | en |
dc.relation.page | 41 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2007-08-27 | |
dc.contributor.author-college | 電機資訊學院 | zh_TW |
dc.contributor.author-dept | 資訊工程學研究所 | zh_TW |
顯示於系所單位: | 資訊工程學系 |
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