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. 2022 Jun 21:2022:3891109.
doi: 10.1155/2022/3891109. eCollection 2022.

Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning

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Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning

Manman Sun et al. Emerg Med Int. .

Retraction in

Abstract

With the rapid development of society and economy, people's living standards are improving day by day, and increasingly attention is paid to physical health, which has set off a fitness upsurge. The purpose of this paper was to analyze the impact of bodybuilding exercise on physical fitness based on deep learning. It provides a reference for fitness enthusiasts to choose scientific and targeted exercise methods, and provides a theoretical basis for the promotion of bodybuilding and fitness. This paper first gives a general introduction to deep learning and adds image segmentation technology to design experiments for bodybuilding and fitness. The experiment was divided into groups A and B, and control group C. In this paper, recurrent neural network and gated recurrent neural network are introduced to compare and analyze the data, and the stability of data processing with different activation functions is compared. The data results show that under the scientific and reasonable arrangement of exercise conditions, bodybuilding and fitness exercises have a corresponding positive effect on the body shape and posture of the subjects. It is more practical to choose a combination of aerobic and anaerobic exercise. In this paper, based on the deep learning algorithm, compared with the recurrent neural network, the gated recurrent neural network is more suitable for processing sequence problems. In the experimental analysis part, this paper compares and analyzes the experimental results of the data under different activation functions, sigmoid function, and tanh function. It is found that the tanh activation function and the gated recurrent neural network are more stable for data processing. The highest AUC value of the traditional recurrent neural network differs by 0.78 from the highest AUC value of the gated recurrent neural network. The data analysis results are in line with the actual situation.

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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 of neurons.
Figure 2
Figure 2
Sigmoid function image (left) and tanh function image (right).
Figure 3
Figure 3
ReLU function image (left) and Elu function image (right).
Figure 4
Figure 4
Simple neural network architecture.
Figure 5
Figure 5
Recurrent neural network model diagram.
Figure 6
Figure 6
Bodybuilding and fitness exercise flow chart.
Figure 7
Figure 7
Some common forms of aerobic exercise.
Figure 8
Figure 8
Some common anaerobic exercise methods.
Figure 9
Figure 9
GRU basic structure.
Figure 10
Figure 10
Gated recurrent unit neural network.
Figure 11
Figure 11
Movement time design.
Figure 12
Figure 12
AUC change graph of GRU under different activation functions.
Figure 13
Figure 13
AUC changes in different neural network models.
Figure 14
Figure 14
Changes in the AUC value of the model in group C.

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