Next Article in Journal
Recent Developments of Magnetoresistive Sensors for Industrial Applications
Previous Article in Journal
Development of a High Irradiance LED Configuration for Small Field of View Motion Estimation of Fertilizer Particles
Article Menu

Export Article

Open AccessArticle
Sensors 2015, 15(11), 28646-28664;

HAGR-D: A Novel Approach for Gesture Recognition with Depth Maps

Escola Politécnica de Pernambuco, Universidade de Pernambuco, R. Benfica, 455-Madalena, Recife-PE 50720-001, Brazil
Author to whom correspondence should be addressed.
Academic Editor: Vittorio M. N. Passaro
Received: 3 August 2015 / Revised: 28 September 2015 / Accepted: 13 October 2015 / Published: 12 November 2015
(This article belongs to the Section Physical Sensors)
Full-Text   |   PDF [675 KB, uploaded 12 November 2015]   |  


The hand is an important part of the body used to express information through gestures, and its movements can be used in dynamic gesture recognition systems based on computer vision with practical applications, such as medical, games and sign language. Although depth sensors have led to great progress in gesture recognition, hand gesture recognition still is an open problem because of its complexity, which is due to the large number of small articulations in a hand. This paper proposes a novel approach for hand gesture recognition with depth maps generated by the Microsoft Kinect Sensor (Microsoft, Redmond, WA, USA) using a variation of the CIPBR (convex invariant position based on RANSAC) algorithm and a hybrid classifier composed of dynamic time warping (DTW) and Hidden Markov models (HMM), called the hybrid approach for gesture recognition with depth maps (HAGR-D). The experiments show that the proposed model overcomes other algorithms presented in the literature in hand gesture recognition tasks, achieving a classification rate of 97.49% in the MSRGesture3D dataset and 98.43% in the RPPDI dynamic gesture dataset. View Full-Text
Keywords: HCI; dynamic gesture; HMM; DTW; CIPBR HCI; dynamic gesture; HMM; DTW; CIPBR

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).

Share & Cite This Article

MDPI and ACS Style

Santos, D.G.; Fernandes, B.J.T.; Bezerra, B.L.D. HAGR-D: A Novel Approach for Gesture Recognition with Depth Maps. Sensors 2015, 15, 28646-28664.

Show more citation formats Show less citations formats

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