IoT-Based Adaptive Oxygen Regulation System with Artificial Neural Networks for Personalized Care

Smart monitoring systems, especially for oxygen treatment management, have advanced in response to the rising need for personalized healthcare solutions. To improve patient care, this research introduces an adaptive oxygen control system based on the Internet of Things (IoT) and uses Artificial Neural Networks (ANNs). The proposed system continually monitors critical metrics, including oxygen levels, patient activity, and ambient variables, Oxygen management is personalized to each patient's specific demands based on real-time analysis of collected data using ANNs. Efficient and personalised oxygen control was shown by the system on Oxygen Flow Prediction dataset, which achieved 96.7% accuracy in forecasting appropriate oxygen flow, reduced oxygen waste by 32%, and improved patient SpO₂ stability by 18%. This adaptive method not only adjusts to the changing needs of each patient but also provides that they are getting the right amount of oxygen. The proposed method is designed to make oxygen control more precise, dependent on human intervention, and better for patients overall. According to experimental data, the system successfully adapts to different patient needs and maintains adequate oxygen levels, which is an important advance in personalized medicine.