Easy gesture recognition for Kinect

Autores
Ibañez, Rodrigo Sebastian; Soria, Alvaro; Teyseyre, Alfredo Raul; Campo, Marcelo Ricardo
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Recent progress in entertainment and gaming systems has brought more natural and intuitive human–computer interfaces to our lives. Innovative technologies, such as Xbox Kinect, enable the recognition of body gestures, which are a direct and expressive way of human communication. Although current development toolkits provide support to identify the position of several joints of the human body and to process the movements of the body parts, they actually lack a flexible and robust mechanism to perform high-level gesture recognition. In consequence, developers are still left with the time-consuming and tedious task of recognizing gestures by explicitly defining a set of conditions on the joint positions and movements of the body parts. This paper presents EasyGR (Easy Gesture Recognition), a tool based on machine learning algorithms that help to reduce the effort involved in gesture recognition. We evaluated EasyGR in the development of 7 gestures, involving 10 developers. We compared time consumed, code size, and the achieved quality of the developed gesture recognizers, with and without the support of EasyGR. The results have shown that our approach is practical and reduces the effort involved in implementing gesture recognizers with Kinect.
Fil: Ibañez, Rodrigo Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Soria, Alvaro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Teyseyre, Alfredo Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Campo, Marcelo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Materia
Natural User Interfaces
Gesture Recognition
Machine Learning
Kinect
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/33617
Acceder
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network_name_str CONICET Digital (CONICET)
spelling Easy gesture recognition for KinectIbañez, Rodrigo SebastianSoria, AlvaroTeyseyre, Alfredo RaulCampo, Marcelo RicardoNatural User InterfacesGesture RecognitionMachine LearningKinecthttps://purl.org/becyt/ford/1.2https://purl.org/becyt/ford/1Recent progress in entertainment and gaming systems has brought more natural and intuitive human–computer interfaces to our lives. Innovative technologies, such as Xbox Kinect, enable the recognition of body gestures, which are a direct and expressive way of human communication. Although current development toolkits provide support to identify the position of several joints of the human body and to process the movements of the body parts, they actually lack a flexible and robust mechanism to perform high-level gesture recognition. In consequence, developers are still left with the time-consuming and tedious task of recognizing gestures by explicitly defining a set of conditions on the joint positions and movements of the body parts. This paper presents EasyGR (Easy Gesture Recognition), a tool based on machine learning algorithms that help to reduce the effort involved in gesture recognition. We evaluated EasyGR in the development of 7 gestures, involving 10 developers. We compared time consumed, code size, and the achieved quality of the developed gesture recognizers, with and without the support of EasyGR. The results have shown that our approach is practical and reduces the effort involved in implementing gesture recognizers with Kinect.Fil: Ibañez, Rodrigo Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Soria, Alvaro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Teyseyre, Alfredo Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaFil: Campo, Marcelo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; ArgentinaElsevier2014-07info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/33617Ibañez, Rodrigo Sebastian; Campo, Marcelo Ricardo; Soria, Alvaro; Teyseyre, Alfredo Raul; Easy gesture recognition for Kinect; Elsevier; Advances in Engineering Software; 76; 7-2014; 171-1800965-9978CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.advengsoft.2014.07.005info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0965997814001161info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:06:10Zoai:ri.conicet.gov.ar:11336/33617instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-03 10:06:10.323CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Easy gesture recognition for Kinect
title Easy gesture recognition for Kinect
spellingShingle Easy gesture recognition for Kinect
Ibañez, Rodrigo Sebastian
Natural User Interfaces
Gesture Recognition
Machine Learning
Kinect
title_short Easy gesture recognition for Kinect
title_full Easy gesture recognition for Kinect
title_fullStr Easy gesture recognition for Kinect
title_full_unstemmed Easy gesture recognition for Kinect
title_sort Easy gesture recognition for Kinect
dc.creator.none.fl_str_mv Ibañez, Rodrigo Sebastian
Soria, Alvaro
Teyseyre, Alfredo Raul
Campo, Marcelo Ricardo
author Ibañez, Rodrigo Sebastian
author_facet Ibañez, Rodrigo Sebastian
Soria, Alvaro
Teyseyre, Alfredo Raul
Campo, Marcelo Ricardo
author_role author
author2 Soria, Alvaro
Teyseyre, Alfredo Raul
Campo, Marcelo Ricardo
author2_role author
author
author
dc.subject.none.fl_str_mv Natural User Interfaces
Gesture Recognition
Machine Learning
Kinect
topic Natural User Interfaces
Gesture Recognition
Machine Learning
Kinect
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.2
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv Recent progress in entertainment and gaming systems has brought more natural and intuitive human–computer interfaces to our lives. Innovative technologies, such as Xbox Kinect, enable the recognition of body gestures, which are a direct and expressive way of human communication. Although current development toolkits provide support to identify the position of several joints of the human body and to process the movements of the body parts, they actually lack a flexible and robust mechanism to perform high-level gesture recognition. In consequence, developers are still left with the time-consuming and tedious task of recognizing gestures by explicitly defining a set of conditions on the joint positions and movements of the body parts. This paper presents EasyGR (Easy Gesture Recognition), a tool based on machine learning algorithms that help to reduce the effort involved in gesture recognition. We evaluated EasyGR in the development of 7 gestures, involving 10 developers. We compared time consumed, code size, and the achieved quality of the developed gesture recognizers, with and without the support of EasyGR. The results have shown that our approach is practical and reduces the effort involved in implementing gesture recognizers with Kinect.
Fil: Ibañez, Rodrigo Sebastian. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Soria, Alvaro. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Teyseyre, Alfredo Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
Fil: Campo, Marcelo Ricardo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tandil. Instituto Superior de Ingeniería del Software. Universidad Nacional del Centro de la Provincia de Buenos Aires. Instituto Superior de Ingeniería del Software; Argentina
description Recent progress in entertainment and gaming systems has brought more natural and intuitive human–computer interfaces to our lives. Innovative technologies, such as Xbox Kinect, enable the recognition of body gestures, which are a direct and expressive way of human communication. Although current development toolkits provide support to identify the position of several joints of the human body and to process the movements of the body parts, they actually lack a flexible and robust mechanism to perform high-level gesture recognition. In consequence, developers are still left with the time-consuming and tedious task of recognizing gestures by explicitly defining a set of conditions on the joint positions and movements of the body parts. This paper presents EasyGR (Easy Gesture Recognition), a tool based on machine learning algorithms that help to reduce the effort involved in gesture recognition. We evaluated EasyGR in the development of 7 gestures, involving 10 developers. We compared time consumed, code size, and the achieved quality of the developed gesture recognizers, with and without the support of EasyGR. The results have shown that our approach is practical and reduces the effort involved in implementing gesture recognizers with Kinect.
publishDate 2014
dc.date.none.fl_str_mv 2014-07
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/33617
Ibañez, Rodrigo Sebastian; Campo, Marcelo Ricardo; Soria, Alvaro; Teyseyre, Alfredo Raul; Easy gesture recognition for Kinect; Elsevier; Advances in Engineering Software; 76; 7-2014; 171-180
0965-9978
CONICET Digital
CONICET
url http://hdl.handle.net/11336/33617
identifier_str_mv Ibañez, Rodrigo Sebastian; Campo, Marcelo Ricardo; Soria, Alvaro; Teyseyre, Alfredo Raul; Easy gesture recognition for Kinect; Elsevier; Advances in Engineering Software; 76; 7-2014; 171-180
0965-9978
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.advengsoft.2014.07.005
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0965997814001161
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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score 13.13397

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