Please use this identifier to cite or link to this item: http://repositorio.inesctec.pt/handle/123456789/5090
Title: Real-Time and Continuous Hand Gesture Spotting: an Approach Based on Artificial Neural Networks
Authors: Neto,P
Pereira,D
Norberto Pires,JN
António Paulo Moreira
Issue Date: 2013
Abstract: New and more natural human-robot interfaces are of crucial interest to the evolution of robotics. This paper addresses continuous and real-time hand gesture spotting, i.e., gesture segmentation plus gesture recognition. Gesture patterns are recognized by using artificial neural networks (ANNs) specifically adapted to the process of controlling an industrial robot. Since in continuous gesture recognition the communicative gestures appear intermittently with the non-communicative, we are proposing a new architecture with two ANNs in series to recognize both kinds of gesture. A data glove is used as interface technology. Experimental results demonstrated that the proposed solution presents high recognition rates (over 99% for a library of ten gestures and over 96% for a library of thirty gestures), low training and learning time and a good capacity to generalize from particular situations.
URI: http://repositorio.inesctec.pt/handle/123456789/5090
http://dx.doi.org/10.1109/icra.2013.6630573
metadata.dc.type: conferenceObject
Publication
Appears in Collections:CRIIS - Other Publications

Files in This Item:
File Description SizeFormat 
P-008-GD2.pdf
  Restricted Access
706.89 kBAdobe PDFView/Open Request a copy


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.