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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 | 周承復(Cheng-Fu Chou) | |
dc.contributor.author | Hao-Hsiang Hsu | en |
dc.contributor.author | 徐浩翔 | zh_TW |
dc.date.accessioned | 2021-06-08T02:06:25Z | - |
dc.date.copyright | 2020-08-24 | |
dc.date.issued | 2020 | |
dc.date.submitted | 2020-08-17 | |
dc.identifier.citation | Y. Bengio. Learning deep architectures for AI. Now Publishers Inc, 2009. D. Carroll. A quantitative test of upper extremity function. Journal of chronic diseases, 18(5):479–491, 1965. C. Chang, S. Wang, and C. Wang. Exploiting moving objects: Multi-robot simultaneous localization and tracking. IEEE Transactions on Automation Science andEngineering, 13(2):810–827, 2016. J. Chen, C. Wang, E. H. Wu, and C. Chou. Simultaneous heterogeneous sensor localization, joint tracking, and upper extremity modeling for stroke rehabilitation.IEEE Systems Journal, pages 1–12, 2020. J. Chung, C. Gulcehre, K. Cho, and Y. Bengio. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555, 2014. J. H. Friedman. Greedy function approximation: a gradient boosting machine. Annals of statistics, pages 1189–1232, 2001. J. H. Friedman. Stochastic gradient boosting. Computational statistics data analysis, 38(4):367–378, 2002. S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735–1780, 1997. D. P. Kingma and J. Ba. Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980, 2014. E. Knippenberg, J. Verbrugghe, I. Lamers, S. Palmaers, A. Timmermans, and A. Spooren. Markerless motion capture systems as training device in neurological rehabilitation: a systematic review of their use, application, target population and efficacy. Journal of neuroengineering and rehabilitation, 14(1):61, 2017. H. Mousavi Hondori and M. Khademi. A review on technical and clinical impact of microsoft kinect on physical therapy and rehabilitation. Journal of medical engineering, 2014, 2014. A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, et al. Pytorch: An imperative style, high-performance deep learning library. In Advances in neural information processing systems, pages 8026–8037, 2019. F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al. Scikit-learn: Machine learning in python. the Journal of machine Learning research, 12:2825–2830, 2011. J. Schmidhuber. Deep learning in neural networks: An overview. 2014. I. Sutskever, O. Vinyals, and Q. V. Le. Sequence to sequence learning with neural networks. In Advances in neural information processing systems, pages 3104–3112,2014. L. Yang, B. Yang, H. Dong, and A. E. Saddik. 3-d markerless tracking of human gait by geometric trilateration of multiple kinects. IEEE Systems Journal, 12(2):1393–1403, 2018. M. Ye, C. Yang, V. Stankovic, L. Stankovic, and A. Kerr. A depth camera motion analysis framework for tele-rehabilitation: Motion capture and person-centric kinematics analysis. IEEE Journal of Selected Topics in Signal Processing, 10(5):877–887, 2016. O. Ťupa, O. Vyšata, A. Procházka, O. Dostál, and M. Schätz. Kinect v2 as a tool for stroke recovery: Pilot study of motion scale monitoring. In 2016 InternationalWorkshop on Computational Intelligence for Multimedia Understanding (IWCIM),pages 1–5, 2016 | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/19576 | - |
dc.description.abstract | 在中風復健系統和應用中,應同時考慮可靠性、準確性和遮擋造成的影響。然而,大多數現有的方法主要關注於解決可靠性及準確性的問題。由於遮擋也是影響醫療判斷的重要因素,為了同時解決這三個重要問題,我們提出了一種異質感測器融合框架由RGB-D相機和可穿戴設備組成,用於遮擋時為復健病患提供可靠的關節位置。為了在補償遮擋時融合多個傳感器的測量結果,我們應用了異質感測同時定位,跟蹤和建模以估算關節和傳感器,並用機器學習的方式構建上肢模型 。基於此模型的虛擬測量用於估計遮擋期間關節的位置。使用模擬生成 與收集來自十位受測者資料的應用結果顯示出,提出的演算法可以得到有遮蔽時誤差 3.9 公分的成果,並且隨著遮擋時間的增加還是能使誤差降至5.9公分。由此本篇論文成功展示了在室內環境下,使用機器學習解決遮蔽造成的問題與影響的能力。 | zh_TW |
dc.description.abstract | In stroke rehabilitation systems and applications,The impact of reliability, accuracy and occlusion should be considered at the same time. However, most existing methods mainly focus on solving reliability and accuracy problems. Since occlusion is also an important factor affecting medical judgment, in order to solve these three important problems at the same time, we propose a heterogeneous sensor fusion framework composed of RGB-D camera and wearable device, it is used to provide reliable joint position for rehabilitation patients when covering. In order to fuse the measurement results of multiple sensors when compensating for occlusion, we applied heterogeneous sensing to simultaneously locate, track and model to estimate joints and sensors, and use machine learning algorithms to build upper limb models.Virtual measurement based on this model used to estimate the position of joints during occlusion. The application results of using simulation to generate and collect data from ten subjects show that the proposed algorithm can obtain an error of 3.9 cm with occlusion, and with the increase of occlusion time, the error can still be reduced to 5.9 cm. As a result, this paper successfully demonstrated the ability to use machine learning to solve the problems and effects of shading in an indoor environment. | en |
dc.description.provenance | Made available in DSpace on 2021-06-08T02:06:25Z (GMT). No. of bitstreams: 1 U0001-1608202013063100.pdf: 1290317 bytes, checksum: b7952ff21701edd7a9d6a3a4d299769f (MD5) Previous issue date: 2020 | en |
dc.description.tableofcontents | Verification Letter from the Oral Examination Committee i Acknowledgements iii 摘要 v Abstract vii Contents ix List of Figures xi List of Tables xiii Chapter 1 Introduction 1 Chapter 2 Dataset 5 Chapter 3 Methodology 9 3.1 Performance Metrics 9 3.2 Gradient Boosting Regression 10 3.3 Seq2Se2 11 3.4 Deep Neural Network 14 Chapter 4 Experiments 19 4.1 Data 19 4.2 Experiment I: Gradient Boosting Regression 20 4.3 Experiment II: Seq2Seq 24 4.4 Experiment III: Deep Neural Network 30 4.5 Comparison and Discussion 34 Chapter 5 Conclusion 39 References 41 | |
dc.language.iso | en | |
dc.title | 基於深度學習的異質性感測器上肢建模用於中風復健 | zh_TW |
dc.title | Upper Extremity Modeling For Stroke Rehabilitation With Heterogeneous Sensors Based On Machine Learning Algorithms | en |
dc.type | Thesis | |
dc.date.schoolyear | 108-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 廖婉君(Wanjiun Liao),吳曉光(Hsiao-kuang Wu),黃志煒(Chih-Wei Huang),蔡子傑(Tzu-Chieh Tsai) | |
dc.subject.keyword | 機器學習,神經網路, | zh_TW |
dc.subject.keyword | Machine Learning,Neural Network, | en |
dc.relation.page | 43 | |
dc.identifier.doi | 10.6342/NTU202003573 | |
dc.rights.note | 未授權 | |
dc.date.accepted | 2020-08-18 | |
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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