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Journal of Healthcare Engineering
Volume 2017, Article ID 3090343, 31 pages
Review Article

A Review on Human Activity Recognition Using Vision-Based Method

1College of Information Science and Engineering, Ocean University of China, Qingdao, China
2Department of Computer Science and Technology, Tsinghua University, Beijing, China

Correspondence should be addressed to Zhen Li; moc.liamg@0310nehzil

Received 22 February 2017; Accepted 11 June 2017; Published 20 July 2017

Academic Editor: Dong S. Park

Copyright © 2017 Shugang Zhang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Human activity recognition (HAR) aims to recognize activities from a series of observations on the actions of subjects and the environmental conditions. The vision-based HAR research is the basis of many applications including video surveillance, health care, and human-computer interaction (HCI). This review highlights the advances of state-of-the-art activity recognition approaches, especially for the activity representation and classification methods. For the representation methods, we sort out a chronological research trajectory from global representations to local representations, and recent depth-based representations. For the classification methods, we conform to the categorization of template-based methods, discriminative models, and generative models and review several prevalent methods. Next, representative and available datasets are introduced. Aiming to provide an overview of those methods and a convenient way of comparing them, we classify existing literatures with a detailed taxonomy including representation and classification methods, as well as the datasets they used. Finally, we investigate the directions for future research.