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Hands-free Head-movement Gesture Recognition using Artificial Neural Networks and the Magnified Gradient Function.
L M King1, H T Nguyen, P B Taylor
1Key University Research Centre for Health Technologies, Faculty of Engineering, University of Technology, Sydney, NSW, AUSTRALIA.
Summary
This study introduces a hands-free head-movement gesture classification system using a novel Magnified Gradient Function (MGF) algorithm. The MGF significantly improves classification accuracy for both able-bodied and disabled users.
Area of Science:
- Biomedical Engineering
- Computer Science
- Artificial Intelligence
Background:
- Hands-free control systems are crucial for assistive technology.
- Existing gesture classification methods face challenges in accuracy and convergence speed.
- Neural networks offer a powerful framework for complex pattern recognition.
Purpose of the Study:
- To develop and evaluate a novel hands-free head-movement gesture classification system.
- To introduce and validate the Magnified Gradient Function (MGF) algorithm for neural networks.
- To assess the performance of the MGF algorithm across diverse user groups.
Main Methods:
- Implementation of a Neural Network architecture.
- Integration of the Magnified Gradient Function (MGF) algorithm to enhance convergence.
- Testing and performance evaluation on both able-bodied and disabled user datasets.
Main Results:
- The MGF algorithm demonstrated a significant improvement in classification accuracy.
- For able-bodied users, accuracy increased from 98.25% to 99.85%.
- For disabled users, accuracy improved from 92.08% to 97.50%.
Conclusions:
- The MGF algorithm is effective in enhancing neural network performance for gesture classification.
- The developed system shows high accuracy and potential for real-world applications in assistive technology.
- The MGF algorithm offers a robust solution for improving convergence and accuracy in neural network-based systems.
