Method and practice of improving fitness training effect based on transfer learning from a biomechanics perspective
Abstract
This study focuses on leveraging transfer learning technology to revolutionize fitness training from a cell and molecular biomechanics perspective. In the era of advanced biotechnology, understanding the minute biomechanical events within cells during exercise is crucial. We aim to apply computer-intelligent control concepts to fitness training, especially aerobics, by delving into the cell and molecular biomechanics. Via in-depth analysis of aerobics training's impact on cells and molecules and smart use of computer tech, a B/S mode simulation model integrating NET and SQL Server is crafted. This model offers a scientific framework for fitness training centered around cell and molecular biomechanics. The ID3 algorithm is then employed to dissect student sports test data related to cell and molecular changes, enabling personalized training plans based on individual cell and molecular traits. To enhance the model, the association rule algorithm is introduced. By scrutinizing extensive cell and molecular biomechanics training data, such as how mechanical forces influence gene expression and protein interactions, hidden patterns and correlative factors are unearthed. This refines the model's accuracy and practicality. During experimentation, comprehensive testing of the association rule algorithm in the context of cell and molecular biomechanics is carried out. Results confirm the viability of the computer-intelligent control-based aerobics training strategy, which effectively boosts fitness training effectiveness at the cell and molecular level. This research pioneers novel approaches for aerobics and other sports, providing valuable insights for optimizing training with respect to cell and molecular biomechanics.
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