Design and Research of the AI Badminton Model Based on the Deep Learning Neural Network

In view of the fact that it is difficult for existing algorithms to identify the movements of a player in an accurate way, this paper puts forward an artificial intelligence (AI) motion model on the basis of the deep learning neural network instruction set architecture (ISA). Firstly, a mobile neural network (MNN) inference engine was utilized to create a new AI sports project-side intelligent practice model. Under this model, a movement can be segmented into a series of decomposition movements, which are recognized and judged separately for the purpose of measuring the entire movement. In order to test its feasibility, the study compares the MNN inference engine with the traditional reasoning engine in terms of their algorithmic capabilities and compares the results obtained through this algorithm and traditional online motion app. Research shows that, in the MNN of the AI sports project proposed in this paper, the datasets of action recognition exceed the results of other inference engines, characterized by lightweight, high performance, and accessibility. Research also demonstrates that the AI sports project model can adapt to the needs of sports projects with a variety of themes and improve the accuracy of movement recognition details.

Automated summary

This paper proposes an artificial intelligence motion model based on the deep learning neural network instruction set architecture. By segmenting movements and recognizing the individual sub-movements, the model improves the accuracy of movement recognition. Experimental results demonstrate the feasibility and performance advantages of the proposed model.