Comprehensive Non-Intrusive Patient Monitoring System using Advanced AI and ML

Current Alzheimer's patient monitoring systems often rely on invasive wearable devices or constant in-person supervision, which can be uncomfortable for patients and may fail to provide continuous, accurate monitoring. These traditional approaches risk missing critical incidents such as falls or unauthorized intrusions, potentially compromising patient safety. In contrast, our advanced monitoring system utilizes non-intrusive, camera-based technologies like Local Binary Patterns Histograms (LBPH) for face recognition and neural networks for activity tracking and fall detection, providing continuous, reliable surveillance without discomfort. State-of-the-art text-to-speech technology for voice assistance further enhances the system's usability, ensuring effective and responsive care for Alzheimer's patients.