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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 周承復(Cheng-Fu Chou) | |
| dc.contributor.author | Jyun-Ci Wang | en |
| dc.contributor.author | 王俊麒 | zh_TW |
| dc.date.accessioned | 2022-11-24T03:13:23Z | - |
| dc.date.available | 2021-11-04 | |
| dc.date.available | 2022-11-24T03:13:23Z | - |
| dc.date.copyright | 2021-11-04 | |
| dc.date.issued | 2021 | |
| dc.date.submitted | 2021-10-19 | |
| dc.identifier.citation | [1] A. Paszke, S. Gross, F. A. Lerer, J. Bradbury, G.Chanan, T. Killeen, Z. Lin, N.Gimelshein, and L.Antiga, et al. Pytorch: An imperative style, highperformance deep learning library. Advances in neural information processing systems, 2019. [2] C. Chang, S. Wang, and C. Wang. Exploiting moving objects: Multirobot simultaneous localization and tracking. IEEE Transactions on Automation Science and Engineering, 2016. [3] Chao Li, Qiaoyong Zhong, Di Xie, and Shiliang Pu. Skeletonbased action recognition with convolutional neural networks. IEEE International Conference on Multimedia and Expo Workshops, 2019. [4] J. Chen, C. Wang, E. Wu, and C. Chou. Simultaneous heterogeneous sensor localization, joint tracking, and upper extremity modeling for stroke rehabilitation. IEEE Systems Journal, page 1–12, 2020. [5] D. Carroll. A quantitative test of upper extremity function. Journal of chronic diseases, 1965. [6] E. Knippenberb, J. Verbrugghe, I. Lamers, S. Palmaers, A. Timmermans, and A. Spooern. Markerless motion capture system as training device in neurological rehabilitaion: a systematic review of their use, application, target population and efficacy. Journal of neuroengineering and rehabilitation, 2017. [7] F. Pedregosa, G. Varoquaux, A. V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, and V.Dubourg, et al. Scikitlearn: Machine learning in python. the Journel of machine Learning research, 2011. [8] 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. [9] HaiTrieu Pham, JungJa Kim, Tan Loc Nguyen, and Yonggwan Won. 3d motion matching algorithm using signature feature descriptor. Multimedia Tools and Applications, 2014. [10] HaoHsiang Hsu. Upper extremity modeling for stroke rehabilitation with heterogeneous sensors based on machine learning algorithms. 2020. [11] J. Schmidhuber. Deep learning in neural networks: An overview. 2014. [12] Jianyu Yang, Y. F. Li, Keyi Wang, Yuan Wu, Giuseppe Altieri, and Massimo Scalia. Mixed signature: An invariant descriptor for 3d motion trajectory perception and recognition. Mathematical Problems in Engineering, 2012. [13] L. Yang, B. Yang, H. Dong, and A.E. Saddik. 3d markerless tracking of human gait by geometric trilateration of multiple kinects. IEEE Systems Journal, 2018. [14] M. Ye, C. Yang, V. Stankovic, L. Stankovic, and A. Kerr. A depth camera motion analysis framework for telerehabilitation: Motion capture and personcentric kinematics analysis. IEEE Journal of Selected Topics in Signal Processing, 2016. [15] Madhuri Panwar, S. Ram Dyuthi, K. Chandra Prakash, Dwaipayan Biswas, Amit Acharyya, Koushik Maharatna, Arvind Gautam, and Ganesh R. Naik. Cnn based approach for activity recognition using a wristworn accelerometer. 2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 2017. [16] O. Tupa, O. Vysata, A. Prochazka, O. Dostal, and M. Schatz. Kinect v2 as a tool for stroke recovery: Pilot study of motion scale monitoring. 2016 International Workshop on Computational Intelligence for Multimedia Understanding(IWCIM), 2016. [17] Y. Bengio. Learning deep architectures for ai. Now Publishers Inc, 2009. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/80701 | - |
| dc.description.abstract | 在中風復健系統中，準確性、可靠性和遮擋是三個重要的關注面向。由於現在多數方法著重在解決前兩者準確性和可靠性的問題，但是遮擋也會對系統判別造成影響，因此我們提出融合異質性的感測器，RGB-D相機以及穿戴式裝置，搭配機器學習的方式建構出上肢模型，可在畫面出現遮擋問題時由模型預測出被遮擋的關節位置，以利系統進行正確的判定，並滿足準確性、可靠性和遮擋的三個問題。 有了一系列復健動作的關節位置軌跡，為了矯正患者進行復健的動作，我們希望能將患者以及標準的復健動作進行比對，提供患者能更有效率進行復健。我們提出signature descriptor來代表整段軌跡，因此只要對signature進行比對就能得出兩段軌跡的相似程度，對患者這次的復健動作打出分數。 | zh_TW |
| dc.description.provenance | Made available in DSpace on 2022-11-24T03:13:23Z (GMT). No. of bitstreams: 1 U0001-1810202112383500.pdf: 1590745 bytes, checksum: f9589d1070199eabf5cb542c211641c9 (MD5) Previous issue date: 2021 | en |
| dc.description.tableofcontents | "Acknowledgements (III) 摘要 (V) Abstract (VII) Contents (IX) List of Figures (XI) Chapter 1 Introduction (1) Chapter 2 Related work (3) 2.1 Heterogeneous Sensor Simultaneous Localization, Tracking, And Modeling (HSSLTAM) (3) 2.2 Multilayer Perceptron (MLP) (5) Chapter 3 Data (7) Chapter 4 Method (11) 4.1 Joint Location Prediction When Occlusion (11) 4.2 Trajectory Similarity Comparison (13) Chapter 5 Experiments (19) 5.1 Experiment I: Convolutional Neural Network Model (19) 5.2 Experiment II: Trajectory Similarity Comparison (23) Chapter 6 Conclusion (31) References (33)" | |
| dc.language.iso | en | |
| dc.subject | 神經網路 | zh_TW |
| dc.subject | 機器學習 | zh_TW |
| dc.subject | Neural Network | en |
| dc.subject | Machine Learning | en |
| dc.title | 上肢建模運用於關節遮擋問題以及軌跡相似度比較 | zh_TW |
| dc.title | Upper Extremity Modeling For Joint Occlusion and Trajectory Similarity Comparison | en |
| dc.date.schoolyear | 109-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 吳曉光(Hsin-Tsai Liu),黃志煒(Chih-Yang Tseng),蔡子傑,呂政修 | |
| dc.subject.keyword | 機器學習,神經網路, | zh_TW |
| dc.subject.keyword | Machine Learning,Neural Network, | en |
| dc.relation.page | 35 | |
| dc.identifier.doi | 10.6342/NTU202103819 | |
| dc.rights.note | 同意授權(限校園內公開) | |
| dc.date.accepted | 2021-10-19 | |
| 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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