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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 洪一平 | |
| dc.contributor.author | Chia-Hao Chang | en |
| dc.contributor.author | 張嘉豪 | zh_TW |
| dc.date.accessioned | 2021-06-17T04:34:03Z | - |
| dc.date.available | 2023-08-14 | |
| dc.date.copyright | 2018-08-14 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-08-10 | |
| dc.identifier.citation | [1] Velloso, E., Bulling, A., & Gellersen, H. (2013, April). MotionMA: motion modelling and analysis by demonstration. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems (pp. 1309-1318). ACM.
[2] Anderson, F., Grossman, T., Matejka, J., & Fitzmaurice, G. (2013, October). YouMove: enhancing movement training with an augmented reality mirror. In Proceedings of the 26th annual ACM symposium on User interface software and technology (pp. 311-320). ACM. [3] Tominaga, J., Kawauchi, K., & Rekimoto, J. (2014, March). Around me: a system with an escort robot providing a sports player's self-images. In Proceedings of the 5th Augmented Human International Conference (p. 43). ACM. [4] Han, P. H., Chen, Y. S., Zhong, Y., Wang, H. L., & Hung, Y. P. (2017, March). My Tai-Chi coaches: an augmented-learning tool for practicing Tai-Chi Chuan. In Proceedings of the 8th Augmented Human International Conference (p. 25). ACM. [5] Kuo, Y.W.: ‘Movement Recognition and Guidance Based on Inertial Measurement Unit: Use Tai-chi Chuan Movement as an Example’, National Taiwan University, 2017 [6] Chua, P.T., Crivella, R., Daly, B., Hu, N., Schaaf, R., Ventura, D., Camill, T., Hodgins, J., and Pausch, R.: ‘Training for Physical Tasks in Virtual Environments: Tai Chi’. Proc. Proceedings of the IEEE Virtual Reality 20032003 pp. Pages [7] U. Yang and G. J. Kim, 'Implementation and Evaluation of “Just Follow Me”: An Immersive, VR-Based, Motion-Training System,' Presence: Teleoperators and Virtual Environments, vol. 11, no. 3, pp. 304-323, 2002. [8] Han, P.-H., Chen, K.-W., Hsieh, C.-H., Huang, Y.-J., and Hung, Y.-P.: ‘Ar-arm: Augmented visualization for guiding arm movement in the first-person perspective’, in Editor (Ed.)^(Eds.): ‘Book Ar-arm: Augmented visualization for guiding arm movement in the first-person perspective’ (ACM, 2016, edn.), pp. 31 [9] Yan, S., Ding, G., Guan, Z., Sun, N., Li, H., and Zhang, L.: ‘OutsideMe: Augmenting Dancer's External Self-Image by Using A Mixed Reality System’, in Editor (Ed.)^(Eds.): ‘Book OutsideMe: Augmenting Dancer's External Self-Image by Using A Mixed Reality System’ (ACM, 2015, edn.), pp. 965-970 [10] Yang, D., Tang, J., Huang, Y., Xu, C., Li, J., Hu, L., ... & Liu, H. (2017, March). TennisMaster: an IMU-based online serve performance evaluation system. In Proceedings of the 8th Augmented Human International Conference (p. 17). ACM. [11] Galluzzi, V., Herman, T., & Polgreen, P. (2015, April). Hand hygiene duration and technique recognition using wrist-worn sensors. In Proceedings of the 14th International Conference on Information Processing in Sensor Networks (pp. 106-117). ACM. [12] Hua Huang and Shan Lin. Toothbrushing monitoring using wrist watch. In Proceedings of the ACM Conference on Embedded Network Sensor Systems (SenSys), pages 202–215. ACM, November 2016. [13] Z. Cao, T. Simon, S.-E. Wei, and Y. Sheikh. Realtime multi-person 2d pose estimation using part affinity fields. In CVPR, 2017. [14] D. Mehta, S. Sridhar, O. Sotnychenko, H. Rhodin, M. Shafiei, H.-P. Seidel, W. Xu, D. Casas, and C. Theobalt. VNect: Real-time 3D Human Pose Estimation with a Single RGB Camera. ACM Transactions on Graphics (TOG), 36(4), 2017. [15] D. Tome, C. Russell, and L. Agapito. Lifting from the deep: Convolutional 3d pose estimation from a single image. arXiv, 2017. [16] Stan Salvador, and Philip Chan. 'FastDTW: Toward accurate dynamic time warping in linear time and space.' Intelligent Data Analysis 11.5 (2007): 561-580. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/70659 | - |
| dc.description.abstract | 近年來，運動的學習除了傳統的上課學習方式外，隨著多媒體設備的發展，例如頭戴式顯示器以及搭載九軸慣性量測單元的智慧手環等等，多媒體教材在各種運動或舞蹈學習上也漸漸廣為使用。人們利用多媒體容易取得的特性，在家中可以隨時隨地自主練習。然而，多媒體教材雖然方便，卻無法達到像真人教練引導時的互動性。學習者往往要手動調整多媒體的播放進度來反覆練習，對於不熟的動作只能透過反覆回放來觀察到動作的細節，因為多媒體的播放速度往往是單一且沒有彈性的。為了解決這個問題，本論文設計了一套即時調整播放速度的太極拳學習系統。使用者會在雙手戴上九軸慣性量測單元的智慧手環，而系統會透過由智慧手環取得到的加速度數據來辨識使用者打到哪個動作，藉著辨識出來的動作與教練當下的動作計算出兩者之間時間的差距，而做出播放速度的調整。當差距越大時，調整的幅度也會越大，讓使用者能在最符合自己學習程度的速度之下進行練習，進而增加學習時的效率。論文最後進行了使用者研究，結果顯示即時速度調整的學習系統對於初學者來說，在縮短對於虛擬教練的延遲時間上，有顯著的效果。 | zh_TW |
| dc.description.abstract | In recent years, traditional way is not the only way to learn exercise. We can also learn from multimedia equipment. Multimedia devices are more and more widely used in various sports and dances studies. For example, head- mounted display (HMD) and smart bracelet with nine axes inertial measurement unit (IMU), etc. People can practice exercise at home anytime by getting multimedia easily. Although multimedia teaching materials are convenient, they cannot achieve the interactive like a real instructor. Learner usually needs to adjust play speed of multimedia by hand when practice repeatedly. Since the play speed of multimedia is frequently unitary and without elasticity, learner can only replay many times to observe the details of unfamiliar movement. To solve this problem, this thesis designs a set of TCC learning system which can adjust play speed immediately. The user will wear smart bracelet with IMU for both hands, and the system will identify which action the user hits by using acceleration data obtained from smart bracelet. The system will calculate the time gap between identifying the action and the action of the coach currently, and then adjust the play speed. When the gap is wider, the range of adjustment will be wider too. The purpose of TCC learning system is to increase the efficiency of learning; moreover, it can help the user to find the suitable speed when practicing. We proceed user study in the end of this thesis. The result shows the real-time speed adjustment learning system has conspicuous effect on shorten the time difference between beginners and virtual coach. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T04:34:03Z (GMT). No. of bitstreams: 1 ntu-107-R05944019-1.pdf: 3010773 bytes, checksum: ae62addb410a041f620986d766723688 (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 口試委員會審定書 i
誌謝 ii 中文摘要 iii ABSTRACT iv CONTENTS v LIST OF FIGURES viii LIST OF TABLES x Chapter 1 Introduction 1 Chapter 2 Related Work 5 2.1 Physical Activity Training 5 2.2 Movement Recognition 5 Chapter 3 Speed Estimation of Movement 7 3.1 Problem Definition 7 3.2 Standard Movement Acquisition 9 3.2.1 Motion Capture 9 3.2.2 Acceleration Component of Wrist 9 3.3 Learner Movement Acquisition 10 3.4 Algorithm 11 3.5 Implement Details 13 3.5.1 Smart Watch Calibration 13 3.5.2 Space Correspondence Alignment 13 3.5.3 Signal Similarity 14 3.5.4 Computing Efficiency Improvement 15 3.6 Sliding Window Size Selection 15 3.6.1 Experiment Design 16 3.6.2 Experiment Result 19 3.7 Evaluation 20 3.7.1 Experiment Design 20 3.7.2 Result 21 3.7.3 Discussion 22 Chapter 4 Time Difference Compensation 23 4.1 Implementation 23 4.2 Study Design 25 4.3 Result 26 4.4 Discussion 27 Chapter 5 Intelligent Tai-Chi-Chuan Learning System 28 5.1 System Overview 28 5.2 Hardware Configuration 29 5.2.1 Head-Mounted Display 29 5.2.2 Smart Bracelet 30 5.2.3 Personal Computer 30 5.3 Speed-Adaptive Mode 31 5.4 Waiting Mode 31 Chapter 6 User Study 32 6.1 Time Difference Distribution 32 6.1.1 Study Design 32 6.1.2 Result 32 6.1.3 Discussion 33 6.2 Speed Adjust Mode 34 6.2.1 Study Design 34 6.2.2 Result 34 6.2.3 Discussion 36 Chapter 7 Conclusions and Future Work 37 APPENDIX 38 REFERENCE 40 | |
| dc.language.iso | en | |
| dc.subject | 即時速度估測 | zh_TW |
| dc.subject | 動作辨識 | zh_TW |
| dc.subject | 穿戴式裝置 | zh_TW |
| dc.subject | 擴增實境 | zh_TW |
| dc.subject | 光學透視頭戴式顯示器 | zh_TW |
| dc.subject | 太極拳 | zh_TW |
| dc.subject | real-time speed estimation | en |
| dc.subject | Tai-Chi Chuan | en |
| dc.subject | optical see-through head-mounted display | en |
| dc.subject | Augmented Reality | en |
| dc.subject | wearable device | en |
| dc.subject | movement recognition | en |
| dc.title | 基於速度估測之適應型太極拳引導系統 | zh_TW |
| dc.title | An Adaptive Tai-Chi-Chuan Guiding System Based on
Speed Estimation of Movement | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳祝嵩,李明穗,莊榮宏,邱志義 | |
| dc.subject.keyword | 太極拳,光學透視頭戴式顯示器,擴增實境,穿戴式裝置,動作辨識,即時速度估測, | zh_TW |
| dc.subject.keyword | Tai-Chi Chuan,optical see-through head-mounted display,Augmented Reality,wearable device,movement recognition,real-time speed estimation, | en |
| dc.relation.page | 42 | |
| dc.identifier.doi | 10.6342/NTU201802901 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-08-10 | |
| dc.contributor.author-college | 電機資訊學院 | zh_TW |
| dc.contributor.author-dept | 資訊網路與多媒體研究所 | zh_TW |
| 顯示於系所單位： | 資訊網路與多媒體研究所 | |
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|---|---|---|---|
| ntu-107-1.pdf 未授權公開取用 | 2.94 MB | Adobe PDF |
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