Abstract
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.
Highlights
In the sustainable exploration of end intelligence, practice and service empowerment take place in the scenario of healthy life with sports, namely, sports artificial intelligence (AI) project [1]
In the sustainable exploration of end intelligence, practice and service empowerment take place in the scenario of healthy life with sports, namely, sports AI project [1]. Projects of this kind contributed greatly to the realization of the core goal of sports digitization and the steady growth of China’s sports population, becoming a crucial step in intelligence sports [2]. e outbreak of the COVID-19 pandemic has added to the difficulty of traditional offline sports and promoted the development of home-based sports in the context of AI technology. rough technological precipitation, home sports are combined with online sports and further empowered by AI technology, intelligent sports or AI sports
In AI sports projects, the output of the instruction set architecture (ISA) model is combined as the final output vector to improve the accuracy of movement recognition
Summary
In the sustainable exploration of end intelligence, practice and service empowerment take place in the scenario of healthy life with sports, namely, sports AI project [1]. 3. Technical Supports e primary technical idea of intelligent motion at the AI sports end is to use the MNN inference engine for reasoning and pose recognition. Technical Supports e primary technical idea of intelligent motion at the AI sports end is to use the MNN inference engine for reasoning and pose recognition Shanghai Sports Science and Technology Group has developed an AI sports automatic testing tool and solved the problems commonly found in traditional testing methods It has realized rapid positioning and regressing badminton nodes online and quantitatively evaluating the calculation accuracy of a model. E basic processing idea of the automatic testing tool is to simulate the actual situation through batch analysis of video sets, collect bone point data (Figure 6), complete the detection of business results, and automatically form a test report. The results are directly rendered to the canvas
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