本論文旨在研製一套無線身體動作捕捉之籃球投籃訓練系統，無線身體動作捕捉由運動感測模組與個人電腦所組成，其中運動感測模組由微控制器、慣性感測器及WiFi傳輸模組整合而成。運動感測模組佩帶於使用者的胸、上手臂、下手臂及手背等部位，該模組可透過慣性感測器擷取人體投籃過程中產生的運動訊號，訊號經由WiFi協定傳輸至個人電腦，再透過一適應性權重調變無跡式卡爾曼濾波器(AWA-based UKF)為基礎的姿態估測演算法轉換成精準的人體姿態，並呈現於人機介面中。在籃球投籃訓練系統部分，本論文提出投籃出手角度、投籃出手速度及投籃出手動作相似度等三個運動指標偵測演算法。演算法依序以手背的姿態角計算投籃出手角度，再利用手背的姿態進行速度估測及零速補償計算投籃出手速度，最後採用動態時間扭曲演算法來比對使用者投籃訊號間的相似程度，並透過樣板挑選方法選出最符合此次訓練的樣板進行投籃動作相似度分析。實驗結果顯示，無線身體動作捕捉網路可準確估測人體投籃動作姿態，平均角度誤差為2.46度；籃球投籃訓練系統能準確量化投籃運動指標，投籃出手角度與投籃出手速度平均誤差分別為5.13度及0.54 m/s。研究結果驗證了無線身體動作捕捉系統應用於籃球投籃訓練之可行性，希冀本系統能幫助籃球運動員更有效率的自我訓練並得到良好的表現。

This thesis presents a wireless body motion capture network (WBMCN) based basketball shooting training system (BSTS). A WBMCN consists of a set of motion sensing modules and a personal computer. Each motion sensing module is composed of a microcontroller, an inertial sensor, and a WiFi module. These modules, worn on the chest, the back of the shooting hand, the upper arm, and the lower arm, capture the motion signals during shooting process. The signals are transmitted to the personal computer through WiFi communication protocol. A shooting motion reconstruction algorithm based on an adaptive weighting adjustment-based unscented Kalman filter (AWA-based UKF) has been developed to transform these inertial signals to accurately estimate shooting movement orientations on a human computer interface. With BSTS, three basketball shooting index analysis algorithms are proposed to detect angle of release, speed of release, and similarity of shooting actions, separately. The angle of release is calculated by the orientation from the back of the hand, the speed of release is acquired by velocity estimation and zero velocity compensation of the orientation from the back of the hand, and the shooting action similarity is calculated by a dynamic time warping (DTW) algorithm and a template selection method. The experimental results show that the proposed WBMCN can estimate shooting movement orientations accurately, and the average root mean square error (RMSE) is 2.46 degree. The proposed BSTS can provide informative basketball shooting indices for performance improvement. The RMSE of angle of release and speed of release are respectively 5.13 degree and 0.54 m/s. The experimental results have successfully validated the proposed system. In the future, we hope this system can help basketball players to improve self-training efficiency and obtain better performance.

[1] S. M. Aglioti, P. Cesari, M. Romani, and C. Urgesi, “Action anticipation and motor resonance in elite basketball players,” Nature Neuroscience, vol. 11, no. 9, pp. 1109-1116, 2008.
[2] J. E. Bortz, “A new mathematical formulation for strapdown inertial navigation,” IEEE Transactions on Aerospace and Electronic Systems, no. 1, pp. 61-66, 1971.
[3] P. J. Brancazio, “Physics of basketball,” American Journal of Physics, vol. 49, no. 4, pp. 356-365, 1981.
[4] R. Chambers, T. J. Gabbett, M. H. Cole, and A. Beard, “The use of wearable microsensors to quantify sport-specific movements,” Sports Medicine, vol. 45, no. 7, pp. 1065-1081, 2015.
[5] C.-M. Chen and L.-H. Chen, “A novel method for slow motion replay detection in broadcast basketball video,” Multimedia Tools and Applications, vol. 74, no. 21, pp. 9573-9593, 2015.
[6] H.-T. Chen, C.-L. Chou, T.-S. Fu, S.-Y. Lee, and B.-S. P. Lin, “Recognizing tactic patterns in broadcast basketball video using player trajectory,” Journal of Visual Communication and Image Representation, vol. 23, no. 6, pp. 932-947, 2012.
[7] F. Erčulj and E. Štrumbelj, “Basketball shot types and shot success in different levels of competitive basketball,” PloS One, vol. 10, no. 6, p. e0128885, 2015.
[8] H. Fourati, N. Manamanni, L. Afilal, and Y. Handrich, “Complementary observer for body segments motion capturing by inertial and magnetic sensors,” IEEE/ASME Transactions on Mechatronics, vol. 19, no. 1, pp. 149-157, 2014.
[9] H. Ghasemzadeh, V. Loseu, and R. Jafari, “Wearable coach for sport training: A quantitative model to evaluate wrist-rotation in golf,” Journal of Ambient Intelligence and Smart Environments, vol. 1, no. 2, pp. 173-184, 2009.
[10] Y.-L. Hsu, C.-L. Chu, Y.-J. Tsai, and J.-S. Wang, “An inertial pen with dynamic time warping recognizer for handwriting and gesture recognition,” IEEE Sensors Journal, vol. 15, no. 1, pp. 154-163, 2015.
[11] B. Jablonski, “Quaternion dynamic time warping,” IEEE Transactions on Signal Processing, vol. 60, no. 3, pp. 1174-1183, 2012.
[12] D. A. James, R. I. Leadbetter, M. R. Neeli, B. J. Burkett, D. V. Thiel, and J. B. Lee, “An integrated swimming monitoring system for the biomechanical analysis of swimming strokes,” Sports Technology, vol. 4, no. 3-4, pp. 141-150, 2011.
[13] M. Jug, J. Perš, B. Dežman, and S. Kovačič, “Trajectory based assessment of coordinated human activity,” in International Conference on Computer Vision Systems. Springer, pp. 534-543, 2003.
[14] S. J. Julier and J. K. Uhlmann, “New extension of the Kalman filter to nonlinear systems,” in AeroSense'97. International Society for Optics and Photonics, pp. 182-193, 1997.
[15] S. J. Julier, J. K. Uhlmann, and H. F. Durrant-Whyte, “A new approach for filtering nonlinear systems,” in American Control Conference, Proceedings of the 1995, vol. 3, pp. 1628-1632, 1995.
[16] D.-J. Jwo and S.-H. Wang, “Adaptive fuzzy strong tracking extended Kalman filtering for GPS navigation,” IEEE Sensors Journal, vol. 7, no. 5, pp. 778-789, 2007.
[17] R. E. Kalman, “A new approach to linear filtering and prediction problems,” Journal of Basic Engineering, vol. 82, no. 1, pp. 35-45, 1960.
[18] H. Koda, K. Sagawa, K. Kuroshima, T. Tsukamoto, K. Urita, and Y. Ishibashi, “3D measurement of forearm and upper arm during throwing motion using body mounted sensor,” Journal of Advanced Mechanical Design, Systems, and Manufacturing, vol. 4, no. 1, pp. 167-178, 2010.
[19] E. Kraft, “A quaternion-based unscented Kalman filter for orientation tracking,” in Proceedings of the Sixth International Conference of Information Fusion, vol. 1, pp. 47-54, 2003.
[20] J. B. Lee, R. B. Mellifont, and B. J. Burkett, “The use of a single inertial sensor to identify stride, step, and stance durations of running gait,” Journal of Science and Medicine in Sport, vol. 13, no. 2, pp. 270-273, 2010.
[21] A. Makni, H. Fourati, and A. Y. Kibangou, “Adaptive kalman filter for MEMS-IMU based attitude estimation under external acceleration and parsimonious use of gyroscopes,” in Control Conference (ECC), 2014 European, pp. 1379-1384, 2014.
[22] S. Miller and R. Bartlett, “The relationship between basketball shooting kinematics, distance and playing position,” Journal of Sports Sciences, vol. 14, no. 3, pp. 243-253, 1996.
[23] R. Pio, “Euler angle transformations,” IEEE Transactions on Automatic Control, vol. 11, no. 4, pp. 707-715, 1966.
[24] F. Rojas, M. Cepero, A. Oña, and M. Gutierrez, “Kinematic adjustments in the basketball jump shot against an opponent,” Ergonomics, vol. 43, no. 10, pp. 1651-1660, 2000.
[25] A. M. Sabatini, “Quaternion-based strap-down integration method for applications of inertial sensing to gait analysis,” Medical and Biological Engineering and Computing, vol. 43, no. 1, pp. 94-101, 2005.
[26] A. M. Sabatini, “Quaternion-based extended Kalman filter for determining orientation by inertial and magnetic sensing,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 7, pp. 1346-1356, 2006.
[27] H. Sakoe and S. Chiba, “Dynamic programming algorithm optimization for spoken word recognition,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 26, no. 1, pp. 43-49, 1978.
[28] M. D. Shuster, “A survey of attitude representations,” Navigation, vol. 8, no. 9, pp. 439-517, 1993.
[29] J. Slawinski, J. Poli, S. Karganovic, C. Khazoom, and D. Dinu, “Effect of fatigue on basketball three points shot kinematics,” in ISBS-Conference Proceedings Archive, vol. 33, no. 1, 2016.
[30] S. Sun, X. Meng, L. Ji, J. Wu, and W.-C. Wong, “Adaptive sensor data fusion in motion capture,” 2010 13th conference on Information fusion (FUSION), pp. 1-8, 2010.
[31] C. M. Tran and L. M. Silverberg, “Optimal release conditions for the free throw in men's basketball,” Journal of Sports Sciences, vol. 26, no. 11, pp. 1147-1155, 2008.
[32] 鄭秉恩，混合慣性與光學感測技術之互動式手寫筆系統之研製，國立成功大學電機工程學系碩士論文，2012。
[33] LSM9DS1 data sheet, ST Microelectronics.
[34] LPC11U35 data sheet, NXP Semiconductor.
[35] BQ24040 data sheet, Texas Instruments.
[36] https://www.amebaiot.com/