Research on Simulation Analysis of Physical Training Based on Deep Learning Algorithm

• Main Authors: Hui, Z. (Author), Jing, C. (Author), Taining, W. (Author)
• Format: Article
• Language: English
• Published: Hindawi Limited 2022
• Subjects: Aging societies; Computer software; Deep learning; Fitness training; Functional fitness; Gait analysis; Global population; Learning algorithms; Natural laws; Pattern recognition; Personnel training; Physical fitness; Physical training; Physiological aging; Simulation analysis; Training simulation
• Online Access: View Fulltext in Publisher

 Aging is the trend of the global population in the 21st century. Physical degradation of the elderly and related care is a major challenge in the face of an aging society. Exercise can delay physiological aging and promote the metabolism of body functions. Although aging is an irreversible natural law, proper physical training can help prevent aging. Therefore, relevant personnel attach great importance to the training of physical fitness. To this end, a 12-week elderly functional fitness training experiment was conducted with elderly residents in a village in Nanjing. In the detection process, the gait analysis system is mainly used for the subject's motion detection and recording and records the data into the gait analysis software system based on the improved deep learning algorithm for sports training simulation analysis. After completing the physical training simulation experiment, the RTM model is used for simulation analysis. The results were evaluated. The evaluation data show that the homogeneity test results of the designed physical training simulation experiment are very reasonable. Since the result is much larger than 0.10, it can be inferred that the results of the physical training simulation analysis have been expected and also meet the national GB/T 31054-2014 standard requirements. © 2022 Zhao Hui et al.