Application and Practice of Motion Capture Technology in Badminton Teaching

[1] Yu, H., Mars, H. V. D., Hastie, P. A., & Kulinna, P. H. (2021). Incorporating a motion analysis app in middle school badminton unit. Journal of Teaching in Physical Education, 1–9. Search in Google Scholar

[2] Chow, D. H. K., & Li, S. S. W. (2021). Effects of sport imagery training and imagery ability on badminton service return in a secondary-school physical education setting. International journal of sport psychology (3), 52. Search in Google Scholar

[3] Ye, H. (2023). Intelligent image processing technology for badminton robot under machine vision of internet of things. International journal of humanoid robotics(6), 20. Search in Google Scholar

[4] Hastie, P. A., Wang, W., Liu, H., & He, Y. (2021). The effects of play practice instruction on the badminton content knowledge of a cohort of chinese physical education majors. Journal of Teaching in Physical Education, 1–9. Search in Google Scholar

[5] Tian, Z. (2015). Research on the principles of interactive teaching and practice teaching with the applications on badminton education in colleges. International Journal of Technology, Management(012), 000. Search in Google Scholar

[6] LiYingda. (2021). Simplified beam element model of badminton batting process based on motion differential equation. Complexity. Search in Google Scholar

[7] Hui, Z., Jing, C., & Taining, W. (2022). Research on simulation analysis of physical training based on deep learning algorithm. Scientific Programming, 2022, 1–11. Search in Google Scholar

[8] Lin, Y. N., Hsia, L. H., & Hwang, G. J. (2022). Fostering motor skills in physical education: a mobile technology-supported icra flipped learning model. Computers & education(Feb.), 177. Search in Google Scholar

[9] Nolasco, S., Amaro, C. M., Roseiro, L., Castro, M. A., & Amaro, A. M. (2022). Hand-arm vibration assessment in badminton athletes during three different movements using two rackets. International Journal of Industrial Ergonomics(88-), 88. Search in Google Scholar

[10] Miguel A. Gómez, Adrián Cid, Rivas, F., Júlia Barreira, & Prieto, J. (2021). Dynamic analysis of scoring performance in elite men’s badminton according to contextual-related variables. Chaos Solitons & Fractals, 151(2), 111295. Search in Google Scholar

[11] Liang, Z., Yin, D., Liu, T., Zhu, Z., Lin, H., & Jin, H. (2021). High perceptual sensitivity to global motion in badminton players. International journal of sport psychology(1), 52. Search in Google Scholar

[12] Li, C. (2020). Badminton motion capture with visual image detection of picking robotics. International Journal of Advanced Robotic Systems, 17(6), 172988142096907. Search in Google Scholar

[13] Qi, Y. (2020). Research on badminton action feature recognition based on improved hmm model. Journal of Intelligent and Fuzzy Systems, 39(4), 5571–5582. Search in Google Scholar

[14] Zhang, S. (2020). Effects of fatigue on biomechanics of forehand smash in badminton. Journal of Vibroengineering, 22(5). Search in Google Scholar

[15] Xipeng, Z., Peng, Z., & Yecheng, C. (2022). Research on badminton teaching technology based on human pose estimation algorithm. Scientific Programming. Search in Google Scholar

[16] Wang, P. (2021). Modeling of badminton intelligent teaching system based on neural network. Wireless Communications and Mobile Computing, 2021(8), 1–10. Search in Google Scholar

[17] Cédric Roure, Cédric Roure, Pasco, D., & Pasco, D. (2018). The impact of learning task design on students’ situational interest in physical education. Journal of Teaching in Physical Education, 37(1), 24–34. Search in Google Scholar

[18] Liu, H., Wang, W., Zhang, C., & Hastie, P. A. (2020). College students’ development of badminton skills and tactical competencies following play practice. Journal of Teaching in Physical Education, 1–9. Search in Google Scholar

[19] Colella, R., Sabina, S., Mincarone, P., & Catarinucci, L. (2023). Semi-passive rfid electronic devices with on-chip sensor fusion capabilities for motion capture and biomechanical analysis. IEEE Sensors Journal, 23, 11672–11681. Search in Google Scholar

[20] Gao, Q., Li, J., Zhu, Y., Wang, S., Liufu, J., & Liu, J. (2023). Hand gesture teleoperation for dexterous manipulators in space station by using monocular hand motion capture. Acta astronautica. Search in Google Scholar

[21] Fonk, R., Schneeweiss, S., Simon, U., & Engelhardt, L. (2021). Hand motion capture from a 3d leap motion controller for a musculoskeletal dynamic simulation. Sensors, 21(4), 1199. Search in Google Scholar

[22] Gao, P., Zhao, D., & Chen, X. (2020). Multi-dimensional data modelling of video image action recognition and motion capture in deep learning framework. IET Image Processing, 14(7), 1257–1264. Search in Google Scholar