Optimization of Artificial Intelligence Model for Badminton Teaching and Training with Wireless Network Support

Abstract

With the rapid development of the Internet of Things and artificial intelligence, the society gradually moves into the era of intelligence, and the research results and intelligent products based on wireless networks come into being. Machine learning algorithms are used to classify and recognize badminton strokes in this research, and a badminton technical feature statistics and pace training system are built on this foundation. By exploring the model characteristics and algorithm training method of the Hidden Markov Model (HMM), this paper proposes a model algorithm with an improved HMM training method for recognizing ten common badminton strokes, including serve, forehand rub, backhand rub, and forehand lunge. Serve, forehand rub, backhand rub, forehand flutter, forehand push, backhand push, forehand pick, backhand pick, and forehand loft are among the 10 typical badminton strokes identified by the algorithm. Our technique can distinguish ten common ball-striking movements in real-time, according to the testing.