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      Handshape Recognition Using Skeletal Data

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          Abstract

          In this paper, a method of handshapes recognition based on skeletal data is described. A new feature vector is proposed. It encodes the relative differences between vectors associated with the pointing directions of the particular fingers and the palm normal. Different classifiers are tested on the demanding dataset, containing 48 handshapes performed 500 times by five users. Two different sensor configurations and significant variation in the hand rotation are considered. The late fusion at the decision level of individual models, as well as a comparative study carried out on a publicly available dataset, are also included.

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          Most cited references 41

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            Bagging predictors

             Leo Breiman (1996)
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              A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting

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                Author and article information

                Journal
                Sensors (Basel)
                Sensors (Basel)
                sensors
                Sensors (Basel, Switzerland)
                MDPI
                1424-8220
                06 August 2018
                August 2018
                : 18
                : 8
                Affiliations
                Department of Computer and Control Engineering, Rzeszow University of Technology, 35-959 Rzeszow, Poland; patrykorganisciak@ 123456gmail.com
                Author notes
                Article
                sensors-18-02577
                10.3390/s18082577
                6111288
                30082649
                © 2018 by the authors.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                Categories
                Article

                Biomedical engineering

                handshape recognition, skeletal data, finger alphabet, sign language

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