Author(s)

Xiaojing Zhou

Corresponding Author

Xiaojing Zhou

Abstract

Cardiopulmonary Function Test of Athletes is the Key to Scientifically and Reasonably Formulate Training Plans. in Order to Solve the Problem of Large Errors in the Existing Cardiorespiratory Function Detection Methods, a Multiple Linear Regression Cardiorespiratory Function Detection Method Based on Particle Swarm Optimization is Proposed. through Significant Difference Correlation Evaluation, the Metabolic Circulation Function in Sports is Analyzed to Realize Comprehensive Evaluation of Athletes' Absolute Strength, Speed Strength and Strength Endurance, and the Internal Relationship between Athletes' Aerobic Metabolism Ability and Anaerobic Metabolism Ability is Obtained. the Results Show That 3 Months Aerobic Exercise Can Obviously Improve the Body Shape and Physiological Function of Young Women. Particle Swarm Optimization is Used to Optimize and Improve the Speed and Accuracy of Cardiopulmonary Function Detection. the Method Can Effectively Improve the Cardiopulmonary Function of Athletes Before and after Aerobic Training, and Has High Modeling Accuracy.

Keywords

Multiple Linear Regression; Particle Swarm Optimization Algorithm; Aerobic Exercise; Body Shape; Function