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Real-Time and Continuous Hand Gesture Spotting: an Approach Based on Artificial Neural Networks

Title
Real-Time and Continuous Hand Gesture Spotting: an Approach Based on Artificial Neural Networks
Type
Article in International Conference Proceedings Book
Year
2013
Authors
Neto, P
(Author)
Other
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Pereira, D
(Author)
Other
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Pires, JN
(Author)
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Conference proceedings International
Pages: 178-183
IEEE International Conference on Robotics and Automation (ICRA)
Karlsruhe, GERMANY, MAY 06-10, 2013
Other information
Authenticus ID: P-008-GD2
Abstract (EN): 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.
Language: English
Type (Professor's evaluation): Scientific
No. of pages: 6
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Real-time and continuous hand gesture spotting: An approach based on artificial neural networks (2013)
Article in International Conference Proceedings Book
Neto, P; Pereira, D; Pires, JN; António Paulo Moreira
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