Next Article in Journal / Special Issue
Socially Assistive Robotics: Robot Exercise Trainer for Older Adults
Previous Article in Journal
Development of a Resilient 3-D Printer for Humanitarian Crisis Response
Previous Article in Special Issue
User’s Emotions and Usability Study of a Brain-Computer Interface Applied to People with Cerebral Palsy
Article Menu
Issue 1 (March) cover image

Export Article

Open AccessArticle
Technologies 2018, 6(1), 31; https://doi.org/10.3390/technologies6010031

Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers

1
School of Rural and Surveying Engineering, National Technical University of Athens, 15780 Zografou, Greece
2
Department of Informatics, Technological Educational Institute of Athens, 12243 Egaleo, Greece
3
RISA Sicherheitsanalysen GmbH, 10707 Berlin, Germany
This paper is an extended version of our paper published in Proceedings of the 10th International Conference on PErvasive Technologies Related to Assistive Environments (PETRA 2017), Island of Rhodes, Greece, 21–23 June 2017.
*
Author to whom correspondence should be addressed.
Received: 18 December 2017 / Revised: 3 March 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
Full-Text   |   PDF [2378 KB, uploaded 9 March 2018]   |  

Abstract

In this paper, we scrutinize the effectiveness of classification techniques in recognizing dance types based on motion-captured human skeleton data. In particular, the goal is to identify poses which are characteristic for each dance performed, based on information on body joints, acquired by a Kinect sensor. The datasets used include sequences from six folk dances and their variations. Multiple pose identification schemes are applied using temporal constraints, spatial information, and feature space distributions for the creation of an adequate training dataset. The obtained results are evaluated and discussed. View Full-Text
Keywords: pose identification; Kinect sensor; motion capture device; classification; dance analysis pose identification; Kinect sensor; motion capture device; classification; dance analysis
Figures

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

Share & Cite This Article

MDPI and ACS Style

Protopapadakis, E.; Voulodimos, A.; Doulamis, A.; Camarinopoulos, S.; Doulamis, N.; Miaoulis, G. Dance Pose Identification from Motion Capture Data: A Comparison of Classifiers. Technologies 2018, 6, 31.

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

1

Comments

[Return to top]
Technologies EISSN 2227-7080 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top