Neural Networks for Smart Augmented Reality Interfaces in Human-Computer Interaction
Abstract
The integration of augmented reality (AR) technologies with human-computer interaction (HCI) has created unprecedented opportunities for immersive computing experiences, yet current AR interfaces often lack the intelligence and adaptability required for seamless user interactions. Existing AR systems struggle with real-time user intent recognition, context-aware content delivery, and personalized interface adaptation, limiting their effectiveness in diverse application domains. This research aims to develop and evaluate a neural network-based framework for creating intelligent AR interfaces that can dynamically adapt to user behavior, environmental context, and interaction patterns in real-time. Our methodology employs a multi-modal deep learning architecture combining convolutional neural networks (CNNs) for visual processing, recurrent neural networks (RNNs) for temporal sequence analysis, and transformer models for attention-based context understanding, trained on a comprehensive dataset of 50,000 AR interaction sessions across various scenarios. The proposed system incorporates reinforcement learning algorithms to enable continuous adaptation and personalization of AR interfaces based on user feedback and behavioral patterns. Experimental results demonstrate that our neural network-enhanced AR interfaces achieve 94.7% accuracy in user intent recognition, reduce interaction latency by 43% compared to traditional AR systems, and improve user satisfaction scores by 38% across diverse application domains including education, healthcare, and industrial training. The system successfully adapts interface elements in real-time, providing contextually relevant information with 89% precision while maintaining smooth 60 FPS performance on mobile AR platforms. This research contributes significantly to the advancement of intelligent AR systems by bridging the gap between artificial intelligence and immersive user interfaces, with immediate applications in smart manufacturing, medical visualization, and educational technologies.