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Human activity recognition for an intelligent knee orthosis

Bibliographic Details
Main Author: Santos, Diliana Maria Barradas Rebelo dos
Publication Date: 2012
Format: Master thesis
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/10362/8493
Summary: Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
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spelling Human activity recognition for an intelligent knee orthosisBiosignalsHuman activity recognitionSignal-processingHidden Markov modelsDissertação para obtenção do Grau de Mestre em Engenharia BiomédicaActivity recognition with body-worn sensors is a large and growing field of research. In this thesis we evaluate the possibility to recognize human activities based on data from biosignal sensors solely placed on or under an existing passive knee orthosis, which will produce the needed information to integrate sensors into the orthosis in the future. The development of active orthotic knee devices will allow population to ambulate in a more natural, efficient and less painful manner than they might with a traditional orthosis. Thus, the term ’active orthosis’ refers to a device intended to increase the ambulatory ability of a person suffering from a knee pathology by applying forces to correct the position only when necessary and thereby make usable over longer periods of time. The contribution of this work is the evaluation of the ability to recognize activities with these restrictions on sensor placement as well as providing a proof-of-concept for the development of an activity recognition system for an intelligent orthosis. We use accelerometers and a goniometer placed on the orthosis and Electromyography (EMG) sensors placed on the skin under the orthosis to measure motion and muscle activity respectively. We segment signals in motion primitives semi-automatically and apply Hidden-Markov-Models (HMM) to classify the isolated motion primitives. We discriminate between seven activities like for example walking stairs up and ascend a hill. In a user study with six participants, we evaluate the systems performance for each of the different biosignal modalities alone as well as the combinations of them. For the best performing combination, we reach an average person-dependent accuracy of 98% and a person-independent accuracy of 79%.Faculdade de Ciências e TecnologiaGamboa, HugoAmma, ChristophSchultz, TanjaRUNSantos, Diliana Maria Barradas Rebelo dos2013-01-10T14:25:38Z20122012-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/8493enginfo:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-22T17:12:08Zoai:run.unl.pt:10362/8493Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T16:43:07.220558Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Human activity recognition for an intelligent knee orthosis
title Human activity recognition for an intelligent knee orthosis
spellingShingle Human activity recognition for an intelligent knee orthosis
Santos, Diliana Maria Barradas Rebelo dos
Biosignals
Human activity recognition
Signal-processing
Hidden Markov models
title_short Human activity recognition for an intelligent knee orthosis
title_full Human activity recognition for an intelligent knee orthosis
title_fullStr Human activity recognition for an intelligent knee orthosis
title_full_unstemmed Human activity recognition for an intelligent knee orthosis
title_sort Human activity recognition for an intelligent knee orthosis
author Santos, Diliana Maria Barradas Rebelo dos
author_facet Santos, Diliana Maria Barradas Rebelo dos
author_role author
dc.contributor.none.fl_str_mv Gamboa, Hugo
Amma, Christoph
Schultz, Tanja
RUN
dc.contributor.author.fl_str_mv Santos, Diliana Maria Barradas Rebelo dos
dc.subject.por.fl_str_mv Biosignals
Human activity recognition
Signal-processing
Hidden Markov models
topic Biosignals
Human activity recognition
Signal-processing
Hidden Markov models
description Dissertação para obtenção do Grau de Mestre em Engenharia Biomédica
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-01-01T00:00:00Z
2013-01-10T14:25:38Z
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/10362/8493
url http://hdl.handle.net/10362/8493
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
publisher.none.fl_str_mv Faculdade de Ciências e Tecnologia
dc.source.none.fl_str_mv reponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron:RCAAP
instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
instacron_str RCAAP
institution RCAAP
reponame_str Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
collection Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
repository.name.fl_str_mv Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
repository.mail.fl_str_mv info@rcaap.pt
_version_ 1833596139533762560

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