Feasibility Study of Mass Sports Fitness Program Based on Neural Network Algorithm

Area of Science:

• Sports Science
• Artificial Intelligence
• Biomedical Engineering

Background:

• Mass sports participation is a global trend, significantly impacting public health and quality of life.
• Modern lifestyles contribute to psychological stress, highlighting the need for effective fitness solutions.
• Existing sports recognition methods may lack efficiency or accuracy for personalized fitness planning.

Purpose of the Study:

• To develop an accurate and efficient sports video recognition algorithm for mass fitness applications.
• To create a generation model for feasible mass sports fitness schemes.
• To ensure the safety and rationality of AI-generated fitness plans.

Main Methods:

• A sports video recognition algorithm utilizing a Backpropagation (BP) neural network.
• Classification of static and dynamic features using the BP neural network.
• Fusion of preliminary recognition results using evidence theory for motion video recognition.

Main Results:

• The proposed algorithm accurately recognizes sports videos.
• The system successfully generates feasible mass sports fitness schemes for multiple users.
• Experimental validation confirms the rationality and safety of the generated fitness plans.

Conclusions:

• The BP neural network-based sports video recognition algorithm is effective for mass fitness.
• This approach can be applied to create personalized and safe fitness programs.
• The study contributes to advancing AI applications in public health and sports science.