Optimization of Human Motion Recognition Information Processing System Based on GA-BP Neural Network Algorithm.

Area of Science:

• Computer Science
• Biomedical Engineering
• Artificial Intelligence

Background:

• Current human motion recognition systems suffer from poor timeliness and low fault tolerance.
• Existing methods exhibit suboptimal recognition accuracy and extended response times, hindering effective motion analysis.

Purpose of the Study:

• To develop an optimized information processing system for human motion recognition.
• To enhance the accuracy and timeliness of human motion recognition using advanced algorithms.

Main Methods:

• Designed a human motion recognition system utilizing dynamic capture recognition technology.
• Employed the Genetic Algorithm-Backpropagation (GA-BP) neural network algorithm for comprehensive motion state analysis.
• Conducted experiments to validate the system's performance and analyze results.

Main Results:

• The GA-BP neural network algorithm demonstrated superior data accuracy and response speed compared to baseline methods.
• The system effectively identifies muscle group changes during human motion.
• Achieved faster and more accurate recognition of motion parameters like trajectory and speed changes.

Conclusions:

• The proposed GA-BP neural network-based system significantly improves human motion recognition accuracy and timeliness.
• The system offers potential for providing customized motion suggestions based on accurate analysis.
• This approach addresses limitations in current human motion recognition technologies.