Intelligent Analysis of Exercise Health Big Data Based on Deep Convolutional Neural Network

Area of Science:

• Artificial Intelligence
• Machine Learning
• Sports Science

Background:

• Deep convolutional neural networks (CNNs) are prominent deep learning methods.
• CNNs utilize local perception to analyze image features and build comprehensive representations.
• Sports health big data analysis requires advanced intelligent systems.

Purpose of the Study:

• To research and analyze sports health big data using deep convolutional neural network algorithms.
• To design an intelligent analysis system for practical sports health applications.
• To develop and validate a platform for emotional fatigue detection using multimodal data.

Main Methods:

• Application of deep convolutional neural networks for brainwave data classification.
• Optimization of the CNN model to enhance accuracy and recall.
• Development of a demonstration platform for emotional fatigue detection with multimodal data feature fusion.
• System testing and validation of the developed platform.

Main Results:

• The CNN model achieved accurate classification of brainwave data.
• The optimized model demonstrated improved accuracy and effectiveness compared to other models.
• The emotional fatigue detection platform successfully performed data acquisition, detection, and feedback.
• The platform verified the model's capability for time-series data feature learning.

Conclusions:

• The developed big data platform meets essential health monitoring data analysis needs.
• The system facilitates effective multi-subject interaction, enhancing health information services.
• The study promotes comprehensive health development through advanced data analysis and monitoring.