Dynamic data processing system for sports training system using internet of things

Area of Science:

• Sports Science
• Data Science
• Wearable Technology

Background:

• The Internet of Things (IoT) is increasingly integrated into sports training systems.
• IoT facilitates optimized health data processing for athletes during workouts.

Purpose of the Study:

• To propose a Dynamic data processing system (DDPS) leveraging IoT for sports training tracking.
• To explore conventional design architectures for sports training monitoring.

Main Methods:

• Utilizing wearable IoT devices for continuous monitoring of athletes' physical activity.
• Implementing a DDPS for efficient data processing and analysis of athlete conditions.
• Employing Deep Neural Networks (DNN) to extract key sports activity features.

Main Results:

• The proposed DDPS method achieved 94.3% accuracy.
• Demonstrated an efficiency ratio of 98.2% with a 24.6% reduction in delay.
• Showcased a 28.8% improvement in error range and 31.2% less energy utilization compared to existing methods.

Conclusions:

• The DDPS system offers a significant advancement in sports training tracking and athlete health analysis.
• The integration of IoT and DNN provides a robust framework for real-time performance monitoring.
• This approach enhances data processing efficiency and accuracy for athletes.