Augmented Reality-Based Patient Tracking in Autonomous Healthcare Systems

Augmented Reality (AR) integration into autonomous healthcare systems has resulted in dramatic improvements for patient monitoring practices. An AR-based patient tracking system employing wearable devices with augmented reality technology delivers real-time data visualization including vital signs together with medical history and treatments plans to healthcare providers. A combined network of IoT sensors with LSTM technology analyzes time-series data to accurately detect abnormal health patterns in patients' metrics through automated continuous data collection. Through the AR interface healthcare professionals experience enhanced situational awareness by gaining access to an intuitive interactive visual display. The research obtains its data from recognized sources MIMIC-IV and PhysioNet which provide an extensive range of patient information including heart rate and oxygen saturation levels and body temperature values. The datasets received preprocessing and augmentation methods that generated varied healthcare examples for clinical medical departments and remote environments. The evaluation system relies on three key performance metrics that measure response time performance and detection accuracy and resource efficiency. The experimental findings show how the AR-based system det ects critical health events with 92% accuracy while it cuts down response time by 85% exceeding standard monitoring techniques. During emergencies the AR interface enables healthcare professionals to make better decisions by providing them with interactive visual data that improves their situational awareness.

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