Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction

Autores
Bettio, Raphael W. de; Silva, André H. C.; Heimfarth, Tales; Freire, André P.; Sá, Alex G. C. de
Año de publicación
2013
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
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.
Facultad de Informática
Materia
Ciencias Informáticas
Video (e.g., tape, disk, DVI)
COMPUTER GRAPHICS
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc/3.0/
Repositorio
SEDICI (UNLP)
Institución
Universidad Nacional de La Plata
OAI Identificador
oai:sedici.unlp.edu.ar:10915/29803

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oai_identifier_str oai:sedici.unlp.edu.ar:10915/29803
network_acronym_str SEDICI
repository_id_str 1329
network_name_str SEDICI (UNLP)
spelling Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interactionBettio, Raphael W. deSilva, André H. C.Heimfarth, TalesFreire, André P.Sá, Alex G. C. deCiencias InformáticasVideo (e.g., tape, disk, DVI)COMPUTER GRAPHICSThe 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.Facultad de Informática2013-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionArticulohttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdf69-75http://sedici.unlp.edu.ar/handle/10915/29803enginfo:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct13-3.pdfinfo:eu-repo/semantics/altIdentifier/issn/1666-6038info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc/3.0/Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:30:10Zoai:sedici.unlp.edu.ar:10915/29803Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:30:10.241SEDICI (UNLP) - Universidad Nacional de La Platafalse
dc.title.none.fl_str_mv Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction
title Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction
spellingShingle Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction
Bettio, Raphael W. de
Ciencias Informáticas
Video (e.g., tape, disk, DVI)
COMPUTER GRAPHICS
title_short Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction
title_full Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction
title_fullStr Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction
title_full_unstemmed Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction
title_sort Model and implementation of body movement recognition using Support Vector Machines and Finite State Machines with cartesian coordinates input for gesture-based interaction
dc.creator.none.fl_str_mv Bettio, Raphael W. de
Silva, André H. C.
Heimfarth, Tales
Freire, André P.
Sá, Alex G. C. de
author Bettio, Raphael W. de
author_facet Bettio, Raphael W. de
Silva, André H. C.
Heimfarth, Tales
Freire, André P.
Sá, Alex G. C. de
author_role author
author2 Silva, André H. C.
Heimfarth, Tales
Freire, André P.
Sá, Alex G. C. de
author2_role author
author
author
author
dc.subject.none.fl_str_mv Ciencias Informáticas
Video (e.g., tape, disk, DVI)
COMPUTER GRAPHICS
topic Ciencias Informáticas
Video (e.g., tape, disk, DVI)
COMPUTER GRAPHICS
dc.description.none.fl_txt_mv 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.
Facultad de Informática
description 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.
publishDate 2013
dc.date.none.fl_str_mv 2013-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
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format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://sedici.unlp.edu.ar/handle/10915/29803
url http://sedici.unlp.edu.ar/handle/10915/29803
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/http://journal.info.unlp.edu.ar/wp-content/uploads/JCST-Oct13-3.pdf
info:eu-repo/semantics/altIdentifier/issn/1666-6038
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc/3.0/
Creative Commons Attribution-NonCommercial 3.0 Unported (CC BY-NC 3.0)
dc.format.none.fl_str_mv application/pdf
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dc.source.none.fl_str_mv reponame:SEDICI (UNLP)
instname:Universidad Nacional de La Plata
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reponame_str SEDICI (UNLP)
collection SEDICI (UNLP)
instname_str Universidad Nacional de La Plata
instacron_str UNLP
institution UNLP
repository.name.fl_str_mv SEDICI (UNLP) - Universidad Nacional de La Plata
repository.mail.fl_str_mv alira@sedici.unlp.edu.ar
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