CNN-LSTM Hybrid Model for Kinematic Feature Analysis and Parabolic Radian Prediction in Basketball Videos. (24th September 2021)

• Record Type: Journal Article
• Authors: Rong, Ji; Chen, Yitong; Yang, Jianxin
• Other Names: Ding Bai Yuan Academic Editor.
• Is Part Of: Computational intelligence and neuroscience. Volume 2021(2021)
• Publication Date: 2021-09-24
• Subjects: Neurosciences -- Data processing -- Periodicals; Computational intelligence -- Periodicals; Computational neuroscience -- Periodicals; 612.80285
• DOI: 10.1155/2021/7844472
• Languages: English
• ISSNs: 1687-5265

Abstract

Abstract : With the improvement of living standards around the world, people's love for sports has also increased; basketball is especially loved by people. It is of great importance to provide sound motor instruction for basketball. To this end, this paper comprehensively investigates the dependence between the optimal release conditions and the corresponding shooting arm movements in basketball players. We carry out kinematic feature analysis of basketball sports videos, propose a hybrid CNN-LSTM model that can predict the arc of the shooting parry, and identify the key movements of the arm joint that produce optimal release velocity, angle, and backspin in short-, mid-, and long-range shots. The experiment demonstrates that the model has three rigid planar links with rotational joints that mimic the shoulder, elbow, and wrist joints of the upper arm, forearm, and hand, which are better at guiding the optimal ball release speed, angle, and backspin for different players with the fastest ball speed being about 4.6 m/s and the slowest being about 1.7 m/s.