Cam-Based Human Activity Recognition for Remote Patient Monitoring and Abnormality Alert System

Rapid advancements in healthcare technology have improved patient monitoring, its speed, accuracy, and security. This research examines technologies like real-time alert systems, pose detection, and Convolutional Neural Networks (CNNs). By integrating YOLO framework with custom CNN and HRNet, monitoring systems identifies anomalous activity with 94.5% accuracy, which helps in elder care and patient security by identifying possible risks early. Pose estimation based on webcams ensuring live monitoring of patients by collecting data on their posture to update their health status in realtime. Automated alert systems guarantee that providers of healthcare are alerted immediately of any health risks arise. The introduction of new advanced monitoring technologies into the healthcare systems gives new meaning to healthcare service provision worldwide, with database evaluations showing that the rate of increased accurate detection surged by an admirable 15% to 20% while the response time decreased by up to 25%. This research also considers problems including factors such as scalability, data security, and adaptability. The findings illustrate how the advanced monitoring methods broaden access to healthcare and improved quality of care, delivered significant information to caretakers through intelligent and responsive patient care systems.