Smart Human Action Monitoring Using RGB And Motion Signals

Abstract

Human action recognition is a vital component of smart monitoring systems, with applications in healthcare, surveillance, and intelligent indoor environments. This project presents a smart human action monitoring system that relies exclusively on RGB images to detect and classify human activities in real time. The system processes image frames to extract essential features such as body posture, joint positions, and movement patterns. These features are then analyzed to recognize common human actions, including sitting, walking, running, drinking, and other daily activities. By leveraging advanced image processing and deep learning techniques, the system achieves high accuracy, robustness to varying lighting conditions, and efficiency suitable for real-time deployment. Experimental results demonstrate the system’s ability to monitor human activity reliably, providing a practical solution for indoor action recognition and smart environment applications.