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Automatic detection of stereotyped hand flapping movements : two different approaches

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Resumo:Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with Autism Spectrum Disorder (ASD). The traditional methods for recording the number of occurrences and duration of stereotypies are insufficient and time consuming. Thus the objective of this study is to automatically detect stereotypical motor movements in real time considering two different approaches. The first approach uses the Microsoft sensor Kinect and gesture recognition algorithms. The second approach uses a trademark device of Texas Instruments with built-in accelerometers and statistical methods to recognize stereotyped movements. The two proposed systems were tested in children with Autism Spectrum Disorders (ASD) and the results are compared. This study provides a valuable tool to monitor stereotypes in order to understand and to cope with this problematic. In the end, it facilitates the identification of relevant behavioral patterns when studying interaction skills in children with ASD.
Autores principais:Gonçalves, Nuno
Outros Autores:Rodrigues, José L.; Costa, Sandra; Soares, Filomena
Assunto:Stereotypical motor movements Kinect sensor Accelerometer Gesture recognition ASD
Ano:2012
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Universidade do Minho
Idioma:inglês
Origem:RepositóriUM - Universidade do Minho
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author Gonçalves, Nuno
author2 Rodrigues, José L.
Costa, Sandra
Soares, Filomena
author2_role author
author
author
author_facet Gonçalves, Nuno
Rodrigues, José L.
Costa, Sandra
Soares, Filomena
author_role author
contributor_name_str_mv Universidade do Minho
country_str PT
creators_json_txt [{\"Person.name\":\"Gonçalves, Nuno\"},{\"Person.name\":\"Rodrigues, José L.\"},{\"Person.name\":\"Costa, Sandra\"},{\"Person.name\":\"Soares, Filomena\"}]
datacite.contributors.contributor.contributorName.fl_str_mv Universidade do Minho
datacite.creators.creator.creatorName.fl_str_mv Gonçalves, Nuno
Rodrigues, José L.
Costa, Sandra
Soares, Filomena
datacite.date.Accepted.fl_str_mv 2012-01-01T00:00:00Z
datacite.date.available.fl_str_mv 2012-12-10T16:14:15Z
datacite.date.embargoed.fl_str_mv 2012-12-10T16:14:15Z
datacite.rights.fl_str_mv http://purl.org/coar/access_right/c_abf2
datacite.subjects.subject.fl_str_mv Stereotypical motor movements
Kinect sensor
Accelerometer
Gesture recognition
ASD
datacite.titles.title.fl_str_mv Automatic detection of stereotyped hand flapping movements : two different approaches
dc.contributor.none.fl_str_mv Universidade do Minho
dc.creator.none.fl_str_mv Gonçalves, Nuno
Rodrigues, José L.
Costa, Sandra
Soares, Filomena
dc.date.Accepted.fl_str_mv 2012-01-01T00:00:00Z
dc.date.available.fl_str_mv 2012-12-10T16:14:15Z
dc.date.embargoed.fl_str_mv 2012-12-10T16:14:15Z
dc.format.none.fl_str_mv application/pdf
dc.identifier.none.fl_str_mv https://hdl.handle.net/1822/21375
dc.language.none.fl_str_mv eng
dc.publisher.none.fl_str_mv IEEE
dc.rights.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.subject.none.fl_str_mv Stereotypical motor movements
Kinect sensor
Accelerometer
Gesture recognition
ASD
dc.title.fl_str_mv Automatic detection of stereotyped hand flapping movements : two different approaches
dc.type.none.fl_str_mv http://purl.org/coar/resource_type/c_5794
description Stereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with Autism Spectrum Disorder (ASD). The traditional methods for recording the number of occurrences and duration of stereotypies are insufficient and time consuming. Thus the objective of this study is to automatically detect stereotypical motor movements in real time considering two different approaches. The first approach uses the Microsoft sensor Kinect and gesture recognition algorithms. The second approach uses a trademark device of Texas Instruments with built-in accelerometers and statistical methods to recognize stereotyped movements. The two proposed systems were tested in children with Autism Spectrum Disorders (ASD) and the results are compared. This study provides a valuable tool to monitor stereotypes in order to understand and to cope with this problematic. In the end, it facilitates the identification of relevant behavioral patterns when studying interaction skills in children with ASD.
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eu_rights_str_mv openAccess
format conferencePaper
fulltext.url.fl_str_mv https://prod-dspace.uminho.pt/bitstreams/4865f127-eed4-4088-9165-dd480823bf07/download
id rum_f8d7753f4e83cea5d7d88d20e9d453f0
identifier.url.fl_str_mv https://hdl.handle.net/1822/21375
instacron_str repositorium
institution Universidade do Minho
instname_str Universidade do Minho
language eng
network_acronym_str rum
network_name_str RepositóriUM - Universidade do Minho
oai_identifier_str oai:repositorium.uminho.pt:1822/21375
organization_str_mv urn:organizationAcronym:repositorium
person_str_mv Gonçalves, Nuno
Rodrigues, José L.
Costa, Sandra
Soares, Filomena
publishDate 2012
publisher.none.fl_str_mv IEEE
reponame_str RepositóriUM - Universidade do Minho
repository_id_str urn:repositoryAcronym:rum
service_str_mv urn:repositoryAcronym:rum
spelling engIEEEporStereotypical motor movements are one of the most common and least understood behaviors occurring in individuals with Autism Spectrum Disorder (ASD). The traditional methods for recording the number of occurrences and duration of stereotypies are insufficient and time consuming. Thus the objective of this study is to automatically detect stereotypical motor movements in real time considering two different approaches. The first approach uses the Microsoft sensor Kinect and gesture recognition algorithms. The second approach uses a trademark device of Texas Instruments with built-in accelerometers and statistical methods to recognize stereotyped movements. The two proposed systems were tested in children with Autism Spectrum Disorders (ASD) and the results are compared. This study provides a valuable tool to monitor stereotypes in order to understand and to cope with this problematic. In the end, it facilitates the identification of relevant behavioral patterns when studying interaction skills in children with ASD.application/pdfporAutomatic detection of stereotyped hand flapping movements : two different approachesGonçalves, NunoRodrigues, José L.Costa, SandraSoares, FilomenaHostingInstitutionOrganizationalUniversidade do Minhoe-mailmailto:repositorium@usdb.uminho.ptrepositorium@usdb.uminho.ptISBNIsPartOf9781467346054DOIIsPartOf10.1109/ROMAN.2012.63437842012-12-10T16:14:15Z20122012-01-01T00:00:00ZHandlehttps://hdl.handle.net/1822/21375http://purl.org/coar/access_right/c_abf2open accessStereotypical motor movementsKinect sensorAccelerometerGesture recognitionASD892124 bytesother research producthttp://purl.org/coar/resource_type/c_5794conference paperhttp://purl.org/coar/access_right/c_abf2application/pdffulltexthttps://prod-dspace.uminho.pt/bitstreams/4865f127-eed4-4088-9165-dd480823bf07/download
spellingShingle Automatic detection of stereotyped hand flapping movements : two different approaches
Gonçalves, Nuno
Stereotypical motor movements
Kinect sensor
Accelerometer
Gesture recognition
ASD
status SINGLETON
subject.fl_str_mv Stereotypical motor movements
Kinect sensor
Accelerometer
Gesture recognition
ASD
title Automatic detection of stereotyped hand flapping movements : two different approaches
title_full Automatic detection of stereotyped hand flapping movements : two different approaches
title_fullStr Automatic detection of stereotyped hand flapping movements : two different approaches
title_full_unstemmed Automatic detection of stereotyped hand flapping movements : two different approaches
title_short Automatic detection of stereotyped hand flapping movements : two different approaches
title_sort Automatic detection of stereotyped hand flapping movements : two different approaches
topic Stereotypical motor movements
Kinect sensor
Accelerometer
Gesture recognition
ASD
topic_facet Stereotypical motor movements
Kinect sensor
Accelerometer
Gesture recognition
ASD
url https://hdl.handle.net/1822/21375
visible 1