Human Activity Recognition: A review

Human activity recognition (HAR) is important in people's daily life, helping in both human-to-human interaction and interpersonal relations. In HAR, many studies are presented to show the best data and the best methods in order to predict activities with the most accuracy possible. These studies have different approaches to the problems that HAR present when the real-time is important. In this paper we aim to present some of the methods that exist as well as some of the existing dataset's and understand the different techniques used. The results show that the CNN's algorithms has better performance than the others, however more work need to be developed namely in production of adequate dataset's for training

Automated summary

Human activity recognition (HAR) is important for enhancing human-to-human interaction and interpersonal relations. Various studies have been conducted to identify the best data and methods for predicting activities with high accuracy in real time. This paper presents existing methods and datasets, and explores different techniques. The results indicate that CNN algorithms outperform others, but further work is needed, particularly in generating adequate training datasets.