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

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.

Simulation Analysis of Physical Fitness Training via Deep Learning Algorithm

“…As an example, the study in [16] analyzed the importance of physical fitness in case of adolescent individuals and based on the results proposed to improve the physical fitness training for service system. The study in [17,18] used image segmentation on deep learning models to design experiments emphasizing on body building and fitness. The recurrent neural network and gated recurrent neural network were used to compare, analyze, and determine the stability of data processing when subjected to different activation functions.…”

Identification of Perception Differences in Personality Factors and Autonomy by Sporting Age Category in Competitive Bodybuilders

“…Much research has focused on psychological training in competitive sport [61,62], but studies on how the athletes' perceptions of the dynamics of personality and implicit autonomy undergo changes in relation to the sporting age category and the athletic experience are relatively few and do not address competitive bodybuilders [63][64][65]. Identifying the perception differences regarding the characteristics of personality and autonomy according to the sports age category, we consider that it will facilitate the expansion of knowledge, highlighting the importance of sports psychology in improving the training process of competitive bodybuilders.…”

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

“…Tis article has been retracted by Hindawi following an investigation undertaken by the publisher [1]. Tis investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process:…”

“…This article has been retracted by Hindawi following an investigation undertaken by the publisher [ 1 ]. This investigation has uncovered evidence of one or more of the following indicators of systematic manipulation of the publication process: Discrepancies in scope Discrepancies in the description of the research reported Discrepancies between the availability of data and the research described Inappropriate citations Incoherent, meaningless and/or irrelevant content included in the article Peer-review manipulation …”