A study on the effect of different machine learning algorithms on soccer footwork recognition under trajectory tracking theory

[1] Adapa, Apurva, Nah, et al. (2018). Factors Influencing the Adoption of Smart Wearable Devices. International Journal of Human-Computer Interaction. Search in Google Scholar

[2] Fu, D., Chen, L., & Cheng, Z. (2021). Integration of Wearable Smart Devices and Internet of Things Technology into Public Physical Education. Hindawi Limited. Search in Google Scholar

[3] Sarivan, I. M., Greiner, J. N., Álvarez, D. D., et al. (2020). Enabling Real-Time Quality Inspection in Smart Manufacturing Through Wearable Smart Devices and Deep Learning. Procedia Manufacturing, 51, 373-380. Search in Google Scholar

[4] Sun, Q., Hu, F., & Hao, Q. (2017). Human Movement Modeling and Activity Perception Based on Fiber-Optic Sensing System. IEEE Transactions on Human-Machine Systems, 44(6), 743-754. Search in Google Scholar

[5] Sun, G., Liu, F., Jiang, L., et al. (2018). Visual Analysis of Human Movement: A Functional Region Perspective. Journal of Computer-Aided Design and Computer Graphics, 30(6), 1073. Search in Google Scholar

[6] Wei, Zeng, Chi-Wing, et al. (2017). A Visual Analytics Design for Studying Rhythm Patterns from Human Daily Movement Data. Visual Informatics. Search in Google Scholar

[7] Muñoz-Organero, Mario, Lotfi, et al. (2016). Human Movement Recognition Based on the Stochastic Characterisation of Acceleration Data. Sensors (14248220). Search in Google Scholar

[8] MehdiKhoury, CheeSengChan, XiaofeiJi, et al. (2017). A Unified Fuzzy Framework for Human Hand Motion Recognition. Springer Berlin Heidelberg. Search in Google Scholar

[9] Li, F., & Pan, J. (2016). Human Motion Recognition Based on Triaxial Accelerometer. Journal of Computer Research and Development. Search in Google Scholar

[10] Uddin, M. A., Joolee, J. B., Alam, A., et al. (2017). Human Action Recognition Using Adaptive Local Motion Descriptor in Spark. IEEE Access, 5, 1-1. Search in Google Scholar

[11] Lu, Y. L., Zhang, X., Gong, S., et al. (2016). Recognition of Multiple Human Motion Patterns Based on MEMS Inertial Sensors. Journal of Chinese Inertial Technology. Search in Google Scholar

[12] Wang, P., Liu, H., Wang, L., et al. (2018). Deep Learning-Based Human Motion Recognition for Predictive Context-Aware Human-Robot Collaboration. CIRP Annals - Manufacturing Technology, 67(1), 17-20. Search in Google Scholar

[13] Sanzari, M., Ntouskos, V., Grazioso, S., et al. (2017). Human Motion Primitive Discovery and Recognition. Search in Google Scholar

[14] Ye, Q., Tan, Z., Qu, C., et al. (2021). Human Motion Recognition Using Three-Dimensional Skeleton Model Based on RGBD Vision System. Journal of Physics: Conference Series, 1754(1), 012175 (5pp). Search in Google Scholar

[15] Tao, Z., Hao, Z., Lei, Y. (2022). Human Motion Mode Recognition Based on Multi-parameter Fusion of Wearable Inertial Module Unit and Flexible Pressure Sensor. Sensors and Materials: An International Journal on Sensor Technology, 3 Pt.2, 34. Search in Google Scholar

[16] Pan, Z., & Li, C. (2020). Robust basketball sports recognition by leveraging motion block estimation. Signal Processing: Image Communication, 83(10), 115784. Search in Google Scholar

[17] Skuratovskii, R., Bazarna, A., Osadhyy, E. (2021). Analysis of Speech MEL Scale and Its Classification as Big Data by Parameterized KNN. Artificial Intelligence, 26(jai2021.26(1)), 42-57. Search in Google Scholar

[18] Sun, C. M. D. (2021). SVM-based global vision system of sports competition and action recognition. Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology, 40(2). Search in Google Scholar

[19] Fu, W., Zhang, H., Huang, F. (2022). Internet-Based Supply Chain Financing-Oriented Risk Assessment Using BP Neural Network and SVM. PLOS ONE, 17. Search in Google Scholar