Simulation of sports training recognition system based on internet of things video behavior analysis

Abstract

The video behavior analysis of the Internet of Things is the apparent characteristics and continuous state of the two-dimensional human body in the time dimension. The patterns representing human behavior can be divided into states based on human body structure, forms based on common features and forms based on temporal features, local spaces, and behaviors based on deep learning. Applying IoT video behavior analysis to sports training will have a huge impact. Physical training is the process of training people to improve their physical fitness, increase courage and endurance, and acquire practical skills. It is the basis of various training courses and an important way to improve human physical fitness. The motion recognition system is a DTW pattern matching method, which uses a finite state to describe the spatial characteristics of the angle between the joint points. This method can accurately meet the low-delay requirements of the motion recognition of each frame and the standard motion. It can match and eliminate the influence of individual differences on motion recognition. At the same time, it can also effectively expand the pattern matching method to recognize newly filled motions, and it is a powerful composite general motion recognition system.