Evaluation Method of Basketball Teaching and Training Effect Based on Wearable Device

Abstract

To reduce errors in the evaluation of basketball players’ training effect and facilitate the coaches to make reasonable training plans, this study proposes an evaluation method of the basketball player training effect based on wearable devices and machine learning algorithms. Wearable devices in social physics are intelligent, and their main components are integrated into clothing and other accessories. They collect data, track activities, and customize the experience to the needs and desires of users. In this study, physiological indicators such as heart rate and hemoglobin are selected as input vectors of the support vector machine (SVM), and the expected value of basketball player training effect evaluation is taken as the output to construct training samples. The parameters of the SVM are set to minimize the evaluation error as the learning objective. The SVM learns training samples and constructs an evaluation model for the training effect of basketball players. We use the method proposed in this study to evaluate the training effect of basketball players through computer simulation of dribble, pass, rebound, layup, and shooting. The experimental results show that the training effect evaluation of basketball players with the proposed method has a low relative error, high classification accuracy, and high practical application value.