Evaluation Method of Basketball Teaching and Training Effect Based on Wearable Device - Li, Shuai; Zhang, Wei (Frontiers in Physics, 2022)
To reduce the evaluation error of basketball players' training effect and facilitate the coaches to make reasonable training plans, this paper proposes an evaluation method of basketball players' training effect based on wearable devices and machine learning algorithm. 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 paper, 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 players' training effect evaluation is taken as output to construct training samples. Parameters of the SVM are set to minimize the evaluation error as the learning objective. SVM learns training samples and constructs an evaluation model for training effect of basketball players. We use the method proposed in this paper 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 low relative error, high classification accuracy and high practical application value.