RETRACTED: Auxiliary research on difficult aerobics exercise training based on fpga and movement recognition technology

Abstract

Abstract Based on case instruction, traditional training systems are based on past rivalries’ case studies and preparing to give strength preparing to Aerobics, uncommon games. The preparation results can't be assessed shrewdly and precisely, and the exhibition of investigation and dynamic examination is poor. To take care of this issue, the reason for unique high impact exercise estimates dependent on human-made consciousness is to understand a preparation system that focuses on Aerobics, the strength and the quality of particular sports. It can be achieved by researching Convolutional Neural Networks (CNN) model systems, such as optimizations and other intelligent features such as an intelligent Convolutional Neural Network (CNN) decision making. The design system architecture framework includes sensors, receivers, databases, and analysis modules. The system's core chip is the human-computer interaction FPGA control module to provide comprehensive training in impressive aerobics intensity quality. The information-gathering module is used for gathering information about gauges, sports and strength training as a language. Trainee information management and training result statistics and query execution through the information management module. Some system software includes a software configuration diagram of the system and booting and landing the system. Through the work cycle of the investigation module, the power of specific vigorous exercise sports is dissected. Test results show that the planned framework can give ongoing, standard strength preparing for Aerobics, explicit activities and improve preparing productivity.