Deep Learning Enabled Human Action Recognition

In a highly dynamic society Human Activity Recognition is a widely used area for the development of systems that are capable of automatically identifying human behavior. Human activity recognition has emerged as the most powerful area for researchers because of the complex and dynamic behavior of humans. Human recognition is implemented in many systems and software due to its large usage. It not only is used today for surveillance and security purposes but also for the physiological understanding of human nature. This tells a better understanding of AI, and deep learning as it utilizes these domains widely. This study represents a hybrid deep learning model with the integration of Long short term memory networks (LSTM) and Convolutional Neural Networks (CNN). Video data is used, and it is converted into multiple frames, this model improves the accuracy of detecting human movement in diverse fields. These results highlighted the potential OF combining spatial and temporal features extraction across sectors such as healthcare, security, sports analytics.