Interfaces gestuais baseados no controlador Leap Motion

Detalhes bibliográficos
Autor(a) principal: Bizarro, João Pedro Pereira
Data de Publicação: 2018
Tipo de documento: Dissertação
Idioma: por
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10400.26/36452
Resumo: In the present, most of the human-machine interactions are based on the use of peripherals such as keyboard and computer mouse. However, the use of such peripherals can create certain limitations in the way people interact with machines, for this reason, there is a need to create natural interfaces. One of the possible approaches that has been proposed involves performing gestures that are recognized by a sensor and interpreted by the computer. The use of hands on a human-machine interface is justified by the fact that the hands are an important element in nonverbal communications. Due to this, in this project several possible gesture interfaces were analyzed, using the Leap Motion sensor. The project was based on the development of methods that allowed the recognition of gestures and their association to an action that the computer should perform. Through the analysis of existing studies in the area and the various methods used to allow a program to classify a data set, a gesture classification system was developed. The classification system has tested to verify its accuracy and precision. Using the knowledge obtained throughout the project, and as proof of concept, an application was developed to demonstrate the usefulness of the classification system in a real situation. This application can recognize a gesture and associate it with a keyboard key, allowing a user to write the message resulting from the gestures he makes. This project main conclusion was that the gesture classification system trained using SVM can make a good separation of the various gestures and with this classify correctly the gestures. Most of problems that arise during the recognition of a gesture are a consequence of the Leap Motion not being able to track correctly the gesture being made.
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spelling Interfaces gestuais baseados no controlador Leap MotionLeap motionHand gestureGesture interfacesGesture classificationClassification systemMotion sensorIn the present, most of the human-machine interactions are based on the use of peripherals such as keyboard and computer mouse. However, the use of such peripherals can create certain limitations in the way people interact with machines, for this reason, there is a need to create natural interfaces. One of the possible approaches that has been proposed involves performing gestures that are recognized by a sensor and interpreted by the computer. The use of hands on a human-machine interface is justified by the fact that the hands are an important element in nonverbal communications. Due to this, in this project several possible gesture interfaces were analyzed, using the Leap Motion sensor. The project was based on the development of methods that allowed the recognition of gestures and their association to an action that the computer should perform. Through the analysis of existing studies in the area and the various methods used to allow a program to classify a data set, a gesture classification system was developed. The classification system has tested to verify its accuracy and precision. Using the knowledge obtained throughout the project, and as proof of concept, an application was developed to demonstrate the usefulness of the classification system in a real situation. This application can recognize a gesture and associate it with a keyboard key, allowing a user to write the message resulting from the gestures he makes. This project main conclusion was that the gesture classification system trained using SVM can make a good separation of the various gestures and with this classify correctly the gestures. Most of problems that arise during the recognition of a gesture are a consequence of the Leap Motion not being able to track correctly the gesture being made.Martins, Nuno Alexandre CidParedes, Simão Pedro Mendes Cruz ReisRepositório ComumBizarro, João Pedro Pereira2021-05-10T11:12:18Z2018-12-132019-06-24T00:00:00Z2019-06-24T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10400.26/36452porinfo: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:RCAAP2022-09-05T15:41:03Zoai:comum.rcaap.pt:10400.26/36452Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T15:16:51.000622Repositório 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 Interfaces gestuais baseados no controlador Leap Motion
title Interfaces gestuais baseados no controlador Leap Motion
spellingShingle Interfaces gestuais baseados no controlador Leap Motion
Bizarro, João Pedro Pereira
Leap motion
Hand gesture
Gesture interfaces
Gesture classification
Classification system
Motion sensor
title_short Interfaces gestuais baseados no controlador Leap Motion
title_full Interfaces gestuais baseados no controlador Leap Motion
title_fullStr Interfaces gestuais baseados no controlador Leap Motion
title_full_unstemmed Interfaces gestuais baseados no controlador Leap Motion
title_sort Interfaces gestuais baseados no controlador Leap Motion
author Bizarro, João Pedro Pereira
author_facet Bizarro, João Pedro Pereira
author_role author
dc.contributor.none.fl_str_mv Martins, Nuno Alexandre Cid
Paredes, Simão Pedro Mendes Cruz Reis
Repositório Comum
dc.contributor.author.fl_str_mv Bizarro, João Pedro Pereira
dc.subject.por.fl_str_mv Leap motion
Hand gesture
Gesture interfaces
Gesture classification
Classification system
Motion sensor
topic Leap motion
Hand gesture
Gesture interfaces
Gesture classification
Classification system
Motion sensor
description In the present, most of the human-machine interactions are based on the use of peripherals such as keyboard and computer mouse. However, the use of such peripherals can create certain limitations in the way people interact with machines, for this reason, there is a need to create natural interfaces. One of the possible approaches that has been proposed involves performing gestures that are recognized by a sensor and interpreted by the computer. The use of hands on a human-machine interface is justified by the fact that the hands are an important element in nonverbal communications. Due to this, in this project several possible gesture interfaces were analyzed, using the Leap Motion sensor. The project was based on the development of methods that allowed the recognition of gestures and their association to an action that the computer should perform. Through the analysis of existing studies in the area and the various methods used to allow a program to classify a data set, a gesture classification system was developed. The classification system has tested to verify its accuracy and precision. Using the knowledge obtained throughout the project, and as proof of concept, an application was developed to demonstrate the usefulness of the classification system in a real situation. This application can recognize a gesture and associate it with a keyboard key, allowing a user to write the message resulting from the gestures he makes. This project main conclusion was that the gesture classification system trained using SVM can make a good separation of the various gestures and with this classify correctly the gestures. Most of problems that arise during the recognition of a gesture are a consequence of the Leap Motion not being able to track correctly the gesture being made.
publishDate 2018
dc.date.none.fl_str_mv 2018-12-13
2019-06-24T00:00:00Z
2019-06-24T00:00:00Z
2021-05-10T11:12:18Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10400.26/36452
url http://hdl.handle.net/10400.26/36452
dc.language.iso.fl_str_mv por
language por
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
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