Abstract
The combination of scientific and technological achievements and sports has found new opportunities to change people’s sports habits. Sport training takes up an increasing proportion of people’s lives. In order to improve the efficiency of sports training and standardize the training actions of players, this article is based on wireless network communication and uses different types of recognition methods in the field of action recognition to build basic classifications. Iterative mutual training to improve generalization performance can reduce the cost of labeling and realize the complementary advantages of different recognition methods, thereby improving the recognition accuracy of human actions. Finally, the algorithm is used to recognize human movements. This method can effectively overcome the problem of differential degradation of base classifiers in the iterative process of collaborative training and further improve the accuracy of human action recognition. The experimental results prove that the motion recognition of wireless network communication proposed in this paper can effectively improve the accuracy of athletes’ movements, which is more than 20% higher than traditional methods, and, under the guidance of standardized movements, can reduce athletes’ sports injuries.
Highlights
The ubiquitous wireless network establishes a connection between everything and everything, so that the interaction of the information network can be realized
With the popularity of network deployment, no matter where we are, we are surrounded by wireless signals
The shielding effect of the target on the wireless network can be used to intelligently perceive the human body, and the traditional wireless network for communication can be evolved into an intelligent network with human body position and motion recognition capabilities
Summary
The ubiquitous wireless network establishes a connection between everything and everything, so that the interaction of the information network can be realized. Processing salient objects instead of all frames makes the algorithm more efficient, but more importantly, it suppresses the interference of background pixels We combine this method with a new combination of local and global descriptors, namely, 3D-SIFT and histogram of oriented optical flow (HOOF). Experimental results show that this method can be used to detect multiposture and partially occluded sports athletes in complex backgrounds, and it provides an effective technical means for detecting the action characteristics of sports games in complex backgrounds [3] These studies have a certain reference effect for this article, but the data samples of these studies are insufficient, and the research time and research methods are too narrow to be widely applicable in reality. The collection and sorting of sports training movement conditions can scientifically formulate sports training prescriptions to maximize the benefits of exercise on physical health
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