Convolutional Neural Networks-based Real-time Gaze Analysis with IoT Integration in User Experience Design

Internet of Things (IoT) technology in user experience design has transformed its use of digital devices and apps. This transformation includes real-time user gaze analysis, which may greatly improve user experience. It uses machine learning to provide real-time gaze analysis and IoT device integration for a smooth and customized user experience. Gaze tracking and analysis are very accurate using machine learning techniques, especially deep learning models. It uses Convolutional Neural Networks (CNNs) to identify and predict user gaze patterns. IoT devices may adjust to user preferences in real time by analyzing eye movements and focus spots, making them more intuitive and immersive. It discusses IoT infrastructure for gaze analysis and user experience improvement. Gaze data from IoT sensors and cameras is analyzed locally or in the cloud using machine learning algorithms. To improve user experience, insights are utilized to change screen content, illumination, and augmented reality feedback. Gaze analysis and IoT integration privacy and ethics are also addressed. This research addresses real-time gaze analysis systems challenges by combining CNNs with IoT. Problems with scalability and practical adaptation impact recent methods, such as attention-based models and cloud computing. The suggested system designs address these issues by seamlessly integrating CNN s with IoT, giving an efficient and context-aware solution for better user experience design. The might transform user experience design by making interfaces more users, flexible, and customized.