Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction

Detalhes bibliográficos
Autor(a) principal: Mendes, Nuno
Data de Publicação: 2017
Outros Autores: Ferrer, João, Vitorino, João, Safeea, Mohammad, Neto, Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10316/102073
https://doi.org/10.1016/j.promfg.2017.07.156
Resumo: This paper presents an intuitive human-robot interaction (HRI) framework for gesture and human behavior recognition. It relies on a vision-based system as interaction technology to classify gestures and a 3-axis accelerometer for behavior classification (stand, walking, etc.). An intelligent system integrates static gesture recognition recurring to artificial neural networks (ANNs) and dynamic gesture recognition using hidden Markov models (HMM). Results show a recognition rate of 95% for a library of 22 gestures and 97% for a library of 6 behaviors. Experiments show a robot controlled using gestures in a HRI process.
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spelling Human Behavior and Hand Gesture Classification for Smart Human-robot InteractionHuman Robot InteractionHuman Behavior RecognitionGesturesSegmentationAccelerometerThis paper presents an intuitive human-robot interaction (HRI) framework for gesture and human behavior recognition. It relies on a vision-based system as interaction technology to classify gestures and a 3-axis accelerometer for behavior classification (stand, walking, etc.). An intelligent system integrates static gesture recognition recurring to artificial neural networks (ANNs) and dynamic gesture recognition using hidden Markov models (HMM). Results show a recognition rate of 95% for a library of 22 gestures and 97% for a library of 6 behaviors. Experiments show a robot controlled using gestures in a HRI process.2017info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10316/102073http://hdl.handle.net/10316/102073https://doi.org/10.1016/j.promfg.2017.07.156eng23519789Mendes, NunoFerrer, JoãoVitorino, JoãoSafeea, MohammadNeto, Pedroinfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2022-09-23T20:43:21Zoai:estudogeral.uc.pt:10316/102073Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T21:19:06.794830Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction
title Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction
spellingShingle Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction
Mendes, Nuno
Human Robot Interaction
Human Behavior Recognition
Gestures
Segmentation
Accelerometer
title_short Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction
title_full Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction
title_fullStr Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction
title_full_unstemmed Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction
title_sort Human Behavior and Hand Gesture Classification for Smart Human-robot Interaction
author Mendes, Nuno
author_facet Mendes, Nuno
Ferrer, João
Vitorino, João
Safeea, Mohammad
Neto, Pedro
author_role author
author2 Ferrer, João
Vitorino, João
Safeea, Mohammad
Neto, Pedro
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Mendes, Nuno
Ferrer, João
Vitorino, João
Safeea, Mohammad
Neto, Pedro
dc.subject.por.fl_str_mv Human Robot Interaction
Human Behavior Recognition
Gestures
Segmentation
Accelerometer
topic Human Robot Interaction
Human Behavior Recognition
Gestures
Segmentation
Accelerometer
description This paper presents an intuitive human-robot interaction (HRI) framework for gesture and human behavior recognition. It relies on a vision-based system as interaction technology to classify gestures and a 3-axis accelerometer for behavior classification (stand, walking, etc.). An intelligent system integrates static gesture recognition recurring to artificial neural networks (ANNs) and dynamic gesture recognition using hidden Markov models (HMM). Results show a recognition rate of 95% for a library of 22 gestures and 97% for a library of 6 behaviors. Experiments show a robot controlled using gestures in a HRI process.
publishDate 2017
dc.date.none.fl_str_mv 2017
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
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status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10316/102073
http://hdl.handle.net/10316/102073
https://doi.org/10.1016/j.promfg.2017.07.156
url http://hdl.handle.net/10316/102073
https://doi.org/10.1016/j.promfg.2017.07.156
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv 23519789
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