Intelligent Recognition System of Sports Athletes’ Wrong Actions Based on AI+IoT

Abstract

How to effectively identify errors in athletes’ movements, and then achieve the goal of correcting or eliminating them, is a problem that the sports world is currently trying to solve. Sports field is a field where sensor-based human movement recognition can play a role. Obtaining human body data through sensors can provide data basis for research on the characteristics of human movement, improvement of sports level, and technical movement analysis of sports. The main research direction of this paper is the AI+IoT joint recognition of human movements. The target human movements involved include standing, walking, running, shooting, jump shot, jumping, dribbling, walking dribbling, and running dribbling. Computer vision, as a branch of the artificial intelligence research field, uses computers to simulate human visual cognitive ability, excavate useful information from digitized images or videos without manual intervention, and strive to achieve human understanding of visual signals, and realize the ability to understand visual signals from a low level. Conversion of data input to high-level knowledge output. The system scoring module is the core functional module of the system. It extracts the angle feature of the trainer’s skeleton data, finds the corresponding frame to correspond, and prompts the trainer’s current standard degree of movement in the form of score. In terms of judgment accuracy, the accuracy of all subjects after watching the complete video is higher than the accuracy of the time blocking point, with extremely significant differences, $r$  = -10.80, df =52, $p<0.001$ . This research improves the universality of target detection and extraction in the real natural environment of the machine vision system and improves the complexity and robustness of human action recognition.