Human Activity Recognition using Machine Learning

Abstract

Working information and classification is one of the most important problems in computer science. Recognizing and identifying actions or tasks performed by humans is the main goal of intelligent video systems. Human activity is used in many applications, from human-machine interaction to surveillance, security and healthcare. Despite continuous efforts, working knowledge in a limitless field is still a difficult task and faces many challenges. In this article, we focus on some of the current research articles on various cognitive functions. This project includes three popular methods to define projects: vision-based (using estimates), practical devices, and smartphones. We will also discuss some advantages and disadvantages of the above methods and give a brief comparison of their accuracy. The results will also show how the visualization method has become a popular method for HAR research today.