Effects of Aerobic Training on Cardiopulmonary Function Based on Multiple Linear Regression Analysis

Area of Science:

• Sports Science
• Biomedical Engineering
• Exercise Physiology

Background:

• Cardiopulmonary function is crucial for athletic performance.
• Assessing cardiopulmonary function aids in optimizing training regimens.
• Existing models may lack precision in detecting training-induced changes.

Purpose of the Study:

• To investigate the impact of aerobic training on cardiopulmonary function.
• To develop an optimized model for cardiopulmonary function testing using particle swarm optimization (PSO).
• To compare the effectiveness of the PSO-based model against traditional methods.

Main Methods:

• Utilizing multiple linear regression combined with particle swarm optimization (PSO) for model construction.
• Implementing a novel cardiopulmonary function test model for athletes.
• Comparing convergence speed and error rates between PSO and traditional multiple linear regression.

Main Results:

• The PSO-based model achieved convergence in approximately 25 iterations, significantly faster than traditional multiple linear regression (41 iterations).
• The PSO method demonstrated a lower convergence error compared to traditional multiple linear regression.
• The developed model showed high accuracy in detecting cardiopulmonary function changes post-aerobic training.

Conclusions:

• Particle swarm optimization significantly enhances the efficiency and accuracy of cardiopulmonary function testing models.
• The PSO-based model provides superior detection performance for cardiopulmonary function in athletes undergoing aerobic training.
• This approach offers a novel and effective method for cardiopulmonary model detection in human physiology.