Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder
<p dir="ltr">Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, c...
محفوظ في:
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| مؤلفون آخرون: | , , |
| منشور في: |
2021
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إضافة وسم
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| _version_ | 1864513555937624064 |
|---|---|
| author | Bilikis Banire (14158833) |
| author2 | Dena Al Thani (14149995) Marwa Qaraqe (10135172) Bilal Mansoor (2541628) |
| author2_role | author author author |
| author_facet | Bilikis Banire (14158833) Dena Al Thani (14149995) Marwa Qaraqe (10135172) Bilal Mansoor (2541628) |
| author_role | author |
| dc.creator.none.fl_str_mv | Bilikis Banire (14158833) Dena Al Thani (14149995) Marwa Qaraqe (10135172) Bilal Mansoor (2541628) |
| dc.date.none.fl_str_mv | 2021-07-15T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s41666-021-00101-y |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Face-Based_Attention_Recognition_Model_for_Children_with_Autism_Spectrum_Disorder/21596865 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Control engineering, mechatronics and robotics Health sciences Health services and systems Information and computing sciences Applied computing Artificial intelligence Artificial Intelligence Computer Science Applications Health Informatics Information Systems |
| dc.title.none.fl_str_mv | Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric feature transformation using a support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach. We conducted an experimental study on different attentional tasks for 46 children (ASD n=20, typically developing children n=26) and explored the limits of the face-based attention recognition model for participant and task differences. Our results show that the geometric feature transformation using an SVM classifier outperforms the CNN approach. Also, attention detection is more generalizable within typically developing children than within ASD groups and within low-attention tasks than within high-attention tasks. This paper highlights the basis for future face-based attentional recognition for real-time learning and clinical attention interventions.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Healthcare Informatics Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s41666-021-00101-y" target="_blank">http://dx.doi.org/10.1007/s41666-021-00101-y</a></p><p dir="ltr">Additional institutions affiliated with: Mechanical Engineering Program - TAMU-Q, Renad Academy</p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c309f301f3f336cef8ef73f920401d8c |
| identifier_str_mv | 10.1007/s41666-021-00101-y |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/21596865 |
| publishDate | 2021 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Face-Based Attention Recognition Model for Children with Autism Spectrum DisorderBilikis Banire (14158833)Dena Al Thani (14149995)Marwa Qaraqe (10135172)Bilal Mansoor (2541628)EngineeringControl engineering, mechatronics and roboticsHealth sciencesHealth services and systemsInformation and computing sciencesApplied computingArtificial intelligenceArtificial IntelligenceComputer Science ApplicationsHealth InformaticsInformation Systems<p dir="ltr">Attention recognition plays a vital role in providing learning support for children with autism spectrum disorders (ASD). The unobtrusiveness of face-tracking techniques makes it possible to build automatic systems to detect and classify attentional behaviors. However, constructing such systems is a challenging task due to the complexity of attentional behavior in ASD. This paper proposes a face-based attention recognition model using two methods. The first is based on geometric feature transformation using a support vector machine (SVM) classifier, and the second is based on the transformation of time-domain spatial features to 2D spatial images using a convolutional neural network (CNN) approach. We conducted an experimental study on different attentional tasks for 46 children (ASD n=20, typically developing children n=26) and explored the limits of the face-based attention recognition model for participant and task differences. Our results show that the geometric feature transformation using an SVM classifier outperforms the CNN approach. Also, attention detection is more generalizable within typically developing children than within ASD groups and within low-attention tasks than within high-attention tasks. This paper highlights the basis for future face-based attentional recognition for real-time learning and clinical attention interventions.</p><h2>Other Information</h2><p dir="ltr">Published in: Journal of Healthcare Informatics Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="http://dx.doi.org/10.1007/s41666-021-00101-y" target="_blank">http://dx.doi.org/10.1007/s41666-021-00101-y</a></p><p dir="ltr">Additional institutions affiliated with: Mechanical Engineering Program - TAMU-Q, Renad Academy</p>2021-07-15T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s41666-021-00101-yhttps://figshare.com/articles/journal_contribution/Face-Based_Attention_Recognition_Model_for_Children_with_Autism_Spectrum_Disorder/21596865CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/215968652021-07-15T06:00:00Z |
| spellingShingle | Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder Bilikis Banire (14158833) Engineering Control engineering, mechatronics and robotics Health sciences Health services and systems Information and computing sciences Applied computing Artificial intelligence Artificial Intelligence Computer Science Applications Health Informatics Information Systems |
| status_str | publishedVersion |
| title | Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder |
| title_full | Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder |
| title_fullStr | Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder |
| title_full_unstemmed | Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder |
| title_short | Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder |
| title_sort | Face-Based Attention Recognition Model for Children with Autism Spectrum Disorder |
| topic | Engineering Control engineering, mechatronics and robotics Health sciences Health services and systems Information and computing sciences Applied computing Artificial intelligence Artificial Intelligence Computer Science Applications Health Informatics Information Systems |