Simulation Analysis of Physical Fitness Training via Deep Learning Algorithm

Abstract

Physical fitness is defined as a person's capacity to perform at a high level in terms of their strength, speed, endurance, coordination, flexibility, agility, and other athletic attributes. The morphological and functional aspects of the human body are intimately linked when it comes to determining one's level of physical fitness. It is possible to categorize physical fitness into two types: healthy physical fitness and competitive physical fitness, depending on the performance and role it plays in various groups of individuals. Competitive physical fitness is built on the foundation of healthy physical fitness, which is the ability of the organ systems to work properly for a particular set of people. Healthful physical fitness is a prerequisite for the future growth of competitive competition. The simulation study of physical fitness training is an important topic, regardless of whether the goal is to achieve healthy physical fitness or competitive physical fitness. This work combines it with deep learning algorithms to propose a strategy MPRN-ATT-LSTM for physical training simulation analysis. First, this work proposes the idea of a hybrid model, which uses a residual network structure (MPRN) with a pooling layer to learn features from time series to reduce the dimension. Then, the extracted feature vector is sent to the LSTM model for further feature extraction. Considering that the LSTM model has high requirements on sequence of input sequence, when input sequence is changed or unreasonable, it may lead to inaccurate feature extraction and affect the classification results. This work solves this problem by adding a self-attention mechanism, which can better focus on information important for classification and give higher weights. Finally, a large number of experiments are carried out in this work to verify the superiority of this method for simulation analysis of physical training.