Sports Training Health Analysis Algorithm Based on Heart Rhythm Feature Extraction and Convolutional Neural Network

Area of Science:

• Sports Science
• Biomedical Engineering
• Artificial Intelligence

Background:

• Advancements in information technology have led to intelligent sports equipment for athletes.
• Current data analysis lacks accessibility for non-professionals, hindering correct exercise practices.
• Analyzing biological signals like heart sounds offers a new avenue for health monitoring.

Purpose of the Study:

• To develop a novel algorithm for analyzing athletes' training health using heart sound signals.
• To leverage heart rhythm features and convolutional neural networks for accurate health status prediction.
• To bridge the gap between raw exercise data and actionable health insights for athletes.

Main Methods:

• Extraction of heart rhythm features from heart sound signals.
• Application of convolutional neural networks (CNNs) for pattern recognition.
• Development of a health analysis algorithm tailored for exercise training contexts.

Main Results:

• Significant improvement in the accuracy of recognizing and predicting athlete training health status.
• Demonstrated effectiveness of the heart sound-based algorithm in a training environment.
• Validation of the proposed approach for athlete health monitoring.

Conclusions:

• Heart sound analysis combined with CNNs offers a powerful tool for monitoring athlete training health.
• The developed algorithm provides a revolutionary approach to personalized sports health analysis.
• This method enhances the accuracy and reliability of predicting athlete well-being during training.