Feasibility Study of Mass Sports Fitness Program Based on Neural Network Algorithm

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 1 - 7
• Main Authors: Li, Jian, Wu, Yejin
• Format: Journal Article
• Language: English
• Published: New York Hindawi 08.08.2022; John Wiley & Sons, Inc
• Subjects: Algorithms; Back propagation networks; Big Data; Data mining; Data processing; Exercise; Feasibility studies; Fuzzy sets; Knowledge discovery; Neural networks; Physical fitness; Quality of life; Sports; China
• ISSN: 1687-5265; 1687-5273; 1687-5273
• DOI: 10.1155/2022/3639157

Mass sports has become a world trend, setting off a new health revolution in the world. Mass fitness programs not only enrich people's lives. It not only relieves the psychological pressure of modern people but also promotes people's health and improves people's quality of life. According to the time-consuming stability of neural network algorithm, this paper proposes a sports video recognition algorithm based on BP neural network. The static and dynamic features are classified by BP neural network, and the basic probability assignment is constructed according to the preliminary recognition results. At the same time, we use evidence theory to fuse the preliminary results and get the results of motion video recognition. It can be applied to the generation model of the feasible scheme of mass sports fitness. Relevant experiments show that the whole model that generates the feasible mass sports fitness scheme can accurately generate the sports fitness scheme of multiple patient users and ensure the rationality and safety of the sports fitness scheme.