Next Article in Journal
A Novel RFID-Based Sensing Method for Low-Cost Bolt Loosening Monitoring
Next Article in Special Issue
Vision-Based Pose Estimation for Robot-Mediated Hand Telerehabilitation
Previous Article in Journal
Implicit Block ACK Scheme for IEEE 802.11 WLANs
Previous Article in Special Issue
An Imaging Sensor-Aided Vision Navigation Approach that Uses a Geo-Referenced Image Database
Article Menu

Export Article

Open AccessArticle
Sensors 2016, 16(2), 161; doi:10.3390/s16020161

An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor

School of Software, Xidian University, Xi’an 710071, China
Author to whom correspondence should be addressed.
Academic Editor: Yajing Shen
Received: 19 November 2015 / Revised: 22 January 2016 / Accepted: 22 January 2016 / Published: 28 January 2016
(This article belongs to the Special Issue Sensors for Robots)
View Full-Text   |   Download PDF [2464 KB, uploaded 28 January 2016]   |  


Continuous human action recognition (CHAR) is more practical in human-robot interactions. In this paper, an online CHAR algorithm is proposed based on skeletal data extracted from RGB-D images captured by Kinect sensors. Each human action is modeled by a sequence of key poses and atomic motions in a particular order. In order to extract key poses and atomic motions, feature sequences are divided into pose feature segments and motion feature segments, by use of the online segmentation method based on potential differences of features. Likelihood probabilities that each feature segment can be labeled as the extracted key poses or atomic motions, are computed in the online model matching process. An online classification method with variable-length maximal entropy Markov model (MEMM) is performed based on the likelihood probabilities, for recognizing continuous human actions. The variable-length MEMM method ensures the effectiveness and efficiency of the proposed CHAR method. Compared with the published CHAR methods, the proposed algorithm does not need to detect the start and end points of each human action in advance. The experimental results on public datasets show that the proposed algorithm is effective and highly-efficient for recognizing continuous human actions. View Full-Text
Keywords: continuous human action recognition; online segmentation; maximum entropy Markov model; Kinect continuous human action recognition; online segmentation; maximum entropy Markov model; Kinect

Figure 1

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. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhu, G.; Zhang, L.; Shen, P.; Song, J. An Online Continuous Human Action Recognition Algorithm Based on the Kinect Sensor. Sensors 2016, 16, 161.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top