Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning
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...
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| Format: | Article |
| Language: | English |
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Wiley
2022-01-01
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| Series: | Emergency Medicine International |
| Online Access: | http://dx.doi.org/10.1155/2022/3891109 |
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| author | Manman Sun Lijun Wang |
| author_facet | Manman Sun Lijun Wang |
| author_sort | Manman Sun |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8924ab0c954a4042979bdd0f58ea3eeb |
| institution | Kabale University |
| issn | 2090-2859 |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Emergency Medicine International |
| spelling | doaj-art-8924ab0c954a4042979bdd0f58ea3eeb2025-02-03T01:32:34ZengWileyEmergency Medicine International2090-28592022-01-01202210.1155/2022/3891109Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep LearningManman Sun0Lijun Wang1College of Sports and LeisureCollege of Physical EducationWith 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.http://dx.doi.org/10.1155/2022/3891109 |
| spellingShingle | Manman Sun Lijun Wang Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning Emergency Medicine International |
| title | Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning |
| title_full | Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning |
| title_fullStr | Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning |
| title_full_unstemmed | Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning |
| title_short | Effect of Bodybuilding and Fitness Exercise on Physical Fitness Based on Deep Learning |
| title_sort | effect of bodybuilding and fitness exercise on physical fitness based on deep learning |
| url | http://dx.doi.org/10.1155/2022/3891109 |
| work_keys_str_mv | AT manmansun effectofbodybuildingandfitnessexerciseonphysicalfitnessbasedondeeplearning AT lijunwang effectofbodybuildingandfitnessexerciseonphysicalfitnessbasedondeeplearning |