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dc.contributor.author
Ibañez, Rodrigo
dc.contributor.author
Soria, Alvaro
dc.contributor.author
Teyseyre, Alfredo Raul
dc.contributor.author
Berdun, Luis Sebastian
dc.contributor.author
Campo, Marcelo Ricardo
dc.contributor.other
Rodrigues, João
dc.contributor.other
Cardoso, Pedro
dc.contributor.other
Monteiro, Jânio
dc.contributor.other
Figueiredo, Mauro
dc.date.available
2021-05-28T15:04:01Z
dc.date.issued
2016
dc.identifier.citation
Ibañez, Rodrigo; Soria, Alvaro; Teyseyre, Alfredo Raul; Berdun, Luis Sebastian; Campo, Marcelo Ricardo; A Comparative Study of Machine Learning Techniques for Gesture Recognition using Kinect; Igi Publ; 2016; 1-22
dc.identifier.isbn
9781522504351
dc.identifier.uri
http://hdl.handle.net/11336/132738
dc.description.abstract
Progress and technological innovation achieved in recent years, particularly in the area of entertainment and games, have promoted the creation of more natural and intuitive human-computer interfaces. Forexample, natural interaction devices such as Microsoft Kinect allow users to explore a more expressive way of human-computer communication by recognizing body gestures. In this context, several SupervisedMachine Learning techniques have been proposed to recognize gestures. However, scarce research works have focused on a comparative study of the behavior of these techniques. Therefore, this chapter presentsan evaluation of 4 Machine Learning techniques by using the Microsoft Research Cambridge (MSRC-12) Kinect gesture dataset, which involves 30 people performing 12 different gestures. Accuracy was evaluated with different techniques obtaining correct-recognition rates close to 100% in some results. Briefly, the experiments performed in this chapter are likely to provide new insights into the application of Machine Learning technique to facilitate the task of gesture recognition.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Igi Publ
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
MACHINE LEARNING
dc.subject
KINECT
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GESTURE RECOGNITION
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SKELETON DATA
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
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CIENCIAS NATURALES Y EXACTAS
dc.title
A Comparative Study of Machine Learning Techniques for Gesture Recognition using Kinect
dc.type
info:eu-repo/semantics/publishedVersion
dc.type
info:eu-repo/semantics/bookPart
dc.type
info:ar-repo/semantics/parte de libro
dc.date.updated
2021-01-27T20:25:49Z
dc.journal.pagination
1-22
dc.journal.pais
Estados Unidos
dc.description.fil
Fil: Ibañez, Rodrigo. 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
dc.description.fil
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
dc.description.fil
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
dc.description.fil
Fil: Berdun, Luis 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
dc.description.fil
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
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.igi-global.com/book/handbook-research-human-computer-interfaces/146921
dc.conicet.paginas
22
dc.source.titulo
Handbook of Research on Human-Computer Interfaces, Developments, and Applications
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