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.
Highlights
With the improvement of the global standard of living, people’s love for sports has increased; in particular, basketball is loved by people
A proper pitching arc can improve the hitting rate. erefore, many studies have been conducted to analyze the kinematic characteristics in basketball sports videos [2, 3]
Good basketball players throw the ball with a nice arc, proper downward spin, and minimal lateral deviation from the optimal shooting plane. ey manipulate their shoulders, elbows, and wrists to produce the best ball speed, angle, and angular velocity at release
Summary
With the improvement of the global standard of living, people’s love for sports has increased; in particular, basketball is loved by people. The work in [10] used a two-dimensional three-state simulation model to investigate the optimal release conditions for free throws and the corresponding arm movement patterns. The main idea of these deep learning methods is to capture the highlevel semantic features of actions based on CNN models. It cannot guide or predict the action of a specific athlete’s basketball shot. We perform 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
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