Subject review: Human activity recognition based on depth images

Human activity recognition (HAR) has becomes a well-known area of study for researchers due to its multiple possible applications. HAR systems are applied in many areas including supervisory nursing care, medical supervision, surveillance, human- machine interaction, gaming and entertainment, etc. However, developing a robust HAR system that incorporates a comprehensive and scalable set of feature spaces is a challenging task. Much research has been done in this area to develop efficient HAR systems.

In this article, a review of the developments in Human Activity Recognition (HAR) will be presented. Human activity recognition works on two basic principles; feature extraction from sensor data and classification of features into action classes. The review presented in this article is hence divided into two parts. The first part deals with feature representation in HAR systems such as Space-time Features, Frequency Features, Local Descriptors, Optical Flow Based, and Skeleton Joints. The second part deals with the classification approaches used in the literature such as Bayesian, and HMM.