IoT and Deep Learning Oxygen Monitoring System for Critical Health Environments

This project develops a cutting-edge oxygen monitoring system designed to ensure safety in critical health environments such as hospitals, ICUs, and laboratories. By integrating IoT technology, the system provides real-time monitoring of oxygen levels and seamless data sharing via cloud platforms for remote access and historical trend analysis.

Deep learning algorithms enhance the system by predicting oxygen supply trends, detecting anomalies, and providing early warnings of potential issues, such as oxygen depletion or leaks. The system generates instant alerts through a mobile app, allowing healthcare professionals to respond promptly. The app also provides live oxygen level tracking, historical data visualization, and recommendations for optimal oxygen management.

Key Features:

• Real-time monitoring of oxygen levels with IoT-enabled remote access.
• Deep learning-powered trend prediction and anomaly detection.
• Cloud-based storage for long-term data analysis and compliance reporting.
• Mobile app with live updates, alerts, and actionable recommendations.

This project includes source code, complete hardware setup, detailed analysis, and a 200-page project report in soft copy, making it an excellent choice for students interested in IoT and AI applications in healthcare technologies.