Application Algorithms for Basketball Training Based on Big Data and Internet of Things
Bo Li, Xiaofeng Wang, Jinting Yao
Abstract
In recent years, the increasing demand for physical health promotes the basketball sports industry’s reform. The latest science and technology enter the sports industry one after another and constantly impact the traditional sports equipment. However, the conventional sports technology scheme is unreasonable, and the sports training management models are obsolete. So, it is impossible to use scientific methods in basketball training, and the overall training effect is not good. This paper proposes a basketball training algorithm using big data and the Internet of Things. The proposed algorithm uses gesture recognition from continuous video movement characteristics of key figures, trajectory characteristics, and background information fusion. It improves the recognition mechanism based on the Shuangliu C3D video basketball player action classification method. Due to the lack of a scientific exercise plan for basketball players, a training plan based on BMI and IoT-enabled big data is devised. The proposed scheme is implemented so that different basketball players customize their own scientific sports training modules.