Elderly Motion Tracking System with AI-Enabled Edge Computing

In the dynamic landscape of technology, the intersection of edge computing with health care holds immense promise, especially in addressing the needs of vulnerable populations such as the elderly. This research focuses on developing a motion detection system using advanced technologies like artificial intelligence and edge computing to integrate with real-time database systems. This system is developed not only to monitor activities but also to analyze activity patterns in the elderly. These patterns have the potential to identify diseases early, preventing unforeseen consequences. Our evaluation analyzes the accuracy of detecting standing and sleeping positions. It demonstrates high precision in identifying standing postures but reveals challenges in recognizing sleeping postures, mainly due to tracking and detection issues in the YOLOv8 model.