臺灣博碩士論文加值系統

在游泳動作分析之領域中，選手之單趟游泳划手次數為一重要之參數，該參數可用以推估選手游泳速度、划手頻率、划手距離等游泳效率參數，然而我們發現到時下之以影像或是以動力學為基礎之游泳分析方法的設備需求高且不易於選手平時訓練使用。在本論文中我們提出一個適用於捷泳動作之計算划手次數的方法，所提出之方法僅需選手配戴慣性感測器即可準確地計算划手次數，我們的方法主要分成兩個階段，首先第一階段為感測資料處理階段，此階段蒐集慣性感測器線性加速度、過濾雜訊資料，以及求取特徵感測點，第二階段為划手動作切割階段，在此階段中，我們採用相關係數的概念，來判斷線性加速度資料中是否有相似的趨勢和計算划手次數。我們將所設計的方法實作於Android平台上，實驗結果顯示所提出之方法能夠準確地切割出划手區段和計算划手次數。

In the field of swimming motion analysis, the number of strokes in a single trip is an important parameter. This parameter can be used to infer the speed of swimming, stroke rate, distance per stroke, and swim efficiency. However, we found that the current arts for analyzing need expensive equipment and cannot be used when training. In this paper, we propose an algorithm for calculating the number strokes of front crawl by wearable inertial devices. Our approach can be divided into two phases. The first one is the sensory data processing phase. In this phase, we gather the inertial sensory values, filter noises, and find feature points. The second phase is the stroke dividing phase. In this phase, we adopt the concept of correlation coefficient concept to judge if the collected linear acceleration values have similar tendencies and then to accumulate strokes. We implement our design on an Android platform. The experiment results indicate that the proposed scheme can precisely dividing sensory values into strokes and calculate number of strokes.

目錄
圖目錄 IV
表目錄 V
第一章 簡介 1
第二章 感測資料處理階段 5
2.1 資料蒐集模組 5
2.2 清除漂浮模組 6
2.3 起點偵測模組 6
第三章 划手波形切割階段 8
第四章 實做結果 13
4.1 實做環境 13
4.2 實驗結果 14
第五章　結論 18
參考文獻 19
附錄-英文論文 23

圖目錄
圖1: 游泳時線性加速度感測數據之變化 2
圖2: 連續划手間之漂浮區段與划手停止前的額外感測訊號 5
圖3: (a)智慧型手機之三軸加速度計指向方向、(b)手機固定方式 13
圖4: 選手A快游波形切割結果 16
圖5: 選手B快游波形切割結果 16
圖6: 選手C慢游波形切割結果 17
圖7: 選手D慢游波形切割結果 17

表目錄
表格 1: 參數設定值 14
表格 2: 划手動作判斷結果 17

[1] J. Pansiot, B. Lo, and G.-Z. Yang. Swimming stroke kinematic analysis with BSN. In Proc.of IEEE International Conference on Body Sensor Networks (BSN), 2010.
[2]P. Siirtola, P. Laurinen, J. Röning, and H. Kinnunen. Efficient accelerometer-based swimming exercise tracking. In Proc. of IEEE Symposium on Computational Intelligence and Data Mining (CIDM), 2011.
[3] R. M. Hagem, S. G. O''Keefe, T. Fickenscher, and D. V. Thiel. Self-contained adaptable optical wireless communications system for stroke rate during swimming. IEEE Sensors Journal, 13(8):3144- 3151, 2013.
[4] F. Dadashi, F. Crettenand, G. P. Millet, and K. Aminian. Front-crawl instantaneous velocity estimation using a wearable inertial measurement unit. Sensors, 12(10):12927- 12939, 2012.
[5] A. Stamm, D. A. James, and D.V. Thiel. Velocity profiling using inertial sensors for freestyle swimming. Sports Engineering, 16(1):1-11, 2013.
[6] M. Bächlin, and G. Tröster. Swimming performance and technique evaluation with wearable acceleration sensors. Pervasive and Mobile Computing, 8(1):68-81, 2012.
[7] M. Bächlin, K. Förster, and G. Tröster. SwimMaster: a wearable assistant for swimmer. In Proc. of the international conference on Ubiquitous computing (UbiComp), 2009.
[8] W. Choi, J. Oh, T. Park, S. Kang, M. Moon, U. Lee, and J. Song. MobyDick: an interactive multi-swimmer exergame. In Proc.of ACM Conference on Embedded Network Sensor Systems (SenSys), 2014.
[9] M.-S. Pan, and H.-W. Lin. A step counting algorithm for smartphone users: design and implementation. IEEE Sensors Journal, 15(4):2296-2305, 2013.
[10] H. Junker, O. Amft, P. Lukowicz, and G. Tröster. Gesture spotting with body-worn inertial sensors to detect user activities. Pattern Recognition, 41(6):2010-2024, 2008.
[11] Q. Li, J. A. Stankovic, M. A. Hanson, A. T. Barth, J. Lach, and G. Zhou. Accurate, fast fall detection using gyroscopes and accelerometer-derived posture information. In Proc. of IEEE International Workshop on Wearable and Implantable Body Sensor Networks(BSN), 2009.
[12] P. Siirtola, P. Laurinen, E. Haapalainen, J. Roning, and H. Kinnunen. Clustering-based activity classification with a wrist-worn accelerometer using basic features. In Proc. of IEEE Symposium on Computational Intelligence and Data Mining(CIDM), 2009.
[13] N. C. Krishnan, C. Juillard, D. Colbry, and S. Panchanathan, Recognition of hand movements using wearable accelerometers. Journal of Ambient Intelligence and Smart Environments, 1(2):143-155, 2009.
[14] T. Le Sage, A. Bindel, P. P. Conway, L. M. Justham, S. E. Slawson, and A. A. West. Embedded programming and real-time signal processing of swimming strokes. Sports Engineering, 14(1):1-14, 2011.
[15] S. Daukantas, V. Marozas, and A. Lukosevicius. Inertial sensor for objective evaluation of swimmer performance. In Proc. of International Biennial Baltic Electronics Conference (BEC), 2008.
[16] B. H. Khoo, B. K. Lee, S. M. N. A. Senanayake, M. N. Arosha, and B. D. Wilson. System for determining within-stroke variations of speed in swimming (SWiSS). In Proc. of IEEE/ASME International Conference on Advanced Intelligent Mechatronics(AIM), 2009.
[17] A. Stamm, D. A. James, R. M. Hagem, and D. V. Thiel. Investigating arm symmetry in swimming using inertial sensors. In Proc. of IEEE Sensors Conference, 2012.
[18] Y. Ohgi. Mems sensor application for the motion analysis in sports science. In Proc. of International Congress of Mechanical Engineering(Cobem), 2005.
[19] F. Dadashi, F. Crettenand, G. Millet, L. Seifert, J. Komar, and K. Aminian. Frontcrawl propulsive phase detection using inertial sensors. Portuguese Journal of Sport Sciences, 11(2):855-858, 2011.
[20] T. Le Sage, A. Bindel, P. Conway, L. Justham, S. Slawson, J. Webster, and A. West. A multi-sensor system for monitoring the performance of elite swimmers. In Proc. of International Joint Conference on e-Business and Telecommunications (ICETE), 2012.
[21] Y. Ohgi, H. Ichikawa, M. Homma, and C. Miyaji. Stroke phase discrimination in breaststroke swimming using a tri-axial acceleration sensor device. Sports Engineering, 6(2):113-123, 2003.
[22] F. Dadashi, G. P. Millet, and K. Aminian. Estimation of front-crawl energy expenditure using wearable inertial measurement units. IEEE Sensors Journal, 14(4):1020- 1027, 2014.