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. 2022 Feb 23:2022:2613318.
doi: 10.1155/2022/2613318. eCollection 2022.

Design of Motion Detection Device in Sports Based on Deep Learning of Internet of Things

Shaolong Li  1 , Changlei Zhou  2   3
Affiliations

Design of Motion Detection Device in Sports Based on Deep Learning of Internet of Things

Shaolong Li et al. Comput Intell Neurosci. .

Retraction in

Abstract

With the improvement of people's income levels in recent years, people have gradually begun to pay more attention to health, and the number of exercise and fitness people has increased year by year. People are gradually willing to pay for sports and fitness, increase sports consumption, and promote the development of the sports and fitness industry. This article aims to study the deep learning based on the Internet of things to make people aware of the importance of sports. Not loving sports is a major problem that contemporary people need to overcome. This article proposes how to design a motion detection device in sports based on deep learning of the Internet of things. Based on the calculation of the economic volume of the deep learning of the Internet of things and the questionnaire survey method, it can be seen that, in today's globalization, although everyone knows the importance of sports, they are unwilling to practice it and would rather spend more time on the Internet. The experimental results of this article show that more than 50% of college students are very interested in sports and fitness, but the actual use is less than 30%, which is not optimistic. In social surveys, this number will be even lower, with only 14% of people interested in sports. Big data is like a "double-edged sword." It not only displays the user's exercise data in front of everyone through the built-in sensors of the mobile phone, but also manages their physical condition through these. How to use the strengths of sports applications at the same time properly disposing of private information is a part of the next development of sports applications that must be faced.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Basic structure diagram of convolutional neural network (CNN).
Figure 2
Figure 2
Schematic diagram of convolution operation.
Figure 3
Figure 3
Flow chart of background subtraction method.
Figure 4
Figure 4
Pictures of athletes in the new era.
Figure 5
Figure 5
Imports to medical treatment.
Figure 6
Figure 6
The structure diagram of the internet of things.
Figure 7
Figure 7
Statistical chart of output value of China's fitness industry.
Figure 8
Figure 8
Bar chart of percentages by gender and educational background.
Figure 9
Figure 9
Combination diagram of men and women attaching importance to sports year by year.
See this image and copyright information in PMC

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