Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/12309
metadata.artigo.dc.title: Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction
metadata.artigo.dc.creator: Bettio, Raphael Winckler de
Silva, André H. C.
Heimfarth, Tales
Freire, André Pimenta
Sá, Alex G. C. de
metadata.artigo.dc.subject: Support Vector Machines (SVM)
Finite State Machines (FSM)
Video games
Computer games
Games – Data processing
Máquinas de vetores de suporte
Jogos para computador
Jogos – Processamento de dados
metadata.artigo.dc.publisher: Universidad Nacional de La Plata
metadata.artigo.dc.date.issued: Oct-2013
metadata.artigo.dc.identifier.citation: BETTIO, R. W. de et al. Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction. Journal of Computer Science and Technology, Buenos Aires, v. 13, n. 2, p. 69-75, Oct. 2013.
metadata.artigo.dc.description.abstract: The growth in the use of gesture-based interaction in video games has highlighted the potential for the use of such interaction method for a wide range of applications. This paper presents the implementation of an enhanced model for gesture recognition as input method for software applications. The model uses Support Vector Machines (SVM) and Finite State Machines (FSM) and the implementation was based on a Kinect R device. The model uses data input based on Cartesian coordinates. The use of Cartesian coordinates enables more flexibility to generalise the use of the model to different applications, when compared to related work encountered in the literature based on accelerometer devices for data input. The results showed that the use of SVM and FSM with Cartesian coordinates as input for gesture-based interaction is very promising. The success rate in gesture recognition was 98%, from a training corpus of 9 sets obtained by recording real users’ gestures. A proof-of-concept implementation of the gesture recognition interaction was performed using the application Google Earth(R). A preliminary acceptance evaluation with users indicated that the interaction with the system via the implementation reported was satisfactory.
metadata.artigo.dc.identifier.uri: http://hdl.handle.net/10915/29803
http://repositorio.ufla.br/jspui/handle/1/12309
metadata.artigo.dc.language: en_US
Appears in Collections:DCC - Artigos publicados em periódicos

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