One size does not fit all: detecting attention in children with autism using machine learning
<div><p>Detecting the attention of children with autism spectrum disorder (ASD) is of paramount importance for desired learning outcome. Teachers often use subjective methods to assess the attention of children with ASD, and this approach is tedious and inefficient due to disparate atten...
محفوظ في:
| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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| الموضوعات: | |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513533705715712 |
|---|---|
| author | Bilikis Banire (14158833) |
| author2 | Dena Al Thani (14149995) Marwa Qaraqe (10135172) |
| author2_role | author author |
| author_facet | Bilikis Banire (14158833) Dena Al Thani (14149995) Marwa Qaraqe (10135172) |
| author_role | author |
| dc.creator.none.fl_str_mv | Bilikis Banire (14158833) Dena Al Thani (14149995) Marwa Qaraqe (10135172) |
| dc.date.none.fl_str_mv | 2023-06-17T03:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1007/s11257-023-09371-0 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/One_size_does_not_fit_all_detecting_attention_in_children_with_autism_using_machine_learning/24934980 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Education Education systems Information and computing sciences Human-centred computing Attention Autism Face-tracking Eye-tracking Machine learning |
| dc.title.none.fl_str_mv | One size does not fit all: detecting attention in children with autism using machine learning |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <div><p>Detecting the attention of children with autism spectrum disorder (ASD) is of paramount importance for desired learning outcome. Teachers often use subjective methods to assess the attention of children with ASD, and this approach is tedious and inefficient due to disparate attentional behavior in ASD. This study explores the attentional behavior of children with ASD and the control group: typically developing (TD) children, by leveraging machine learning and unobtrusive technologies such as webcams and eye-tracking devices to detect attention objectively. Person-specific and generalized machine models for face-based, gaze-based, and hybrid-based (face and gaze) are proposed in this paper. The performances of these three models were compared, and the gaze-based model outperformed the others. Also, the person-specific model achieves higher predictive power than the generalized model for the ASD group. These findings stress the direction of model design from traditional one-size-fits-all models to personalized models.</p><p> </p></div><h2>Other Information</h2> <p> Published in: User Modeling and User-Adapted Interaction<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="https://dx.doi.org/10.1007/s11257-023-09371-0" target="_blank">https://dx.doi.org/10.1007/s11257-023-09371-0</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_fb1b4be66727bfc61eee12db86e9022d |
| identifier_str_mv | 10.1007/s11257-023-09371-0 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/24934980 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | One size does not fit all: detecting attention in children with autism using machine learningBilikis Banire (14158833)Dena Al Thani (14149995)Marwa Qaraqe (10135172)EducationEducation systemsInformation and computing sciencesHuman-centred computingAttentionAutismFace-trackingEye-trackingMachine learning<div><p>Detecting the attention of children with autism spectrum disorder (ASD) is of paramount importance for desired learning outcome. Teachers often use subjective methods to assess the attention of children with ASD, and this approach is tedious and inefficient due to disparate attentional behavior in ASD. This study explores the attentional behavior of children with ASD and the control group: typically developing (TD) children, by leveraging machine learning and unobtrusive technologies such as webcams and eye-tracking devices to detect attention objectively. Person-specific and generalized machine models for face-based, gaze-based, and hybrid-based (face and gaze) are proposed in this paper. The performances of these three models were compared, and the gaze-based model outperformed the others. Also, the person-specific model achieves higher predictive power than the generalized model for the ASD group. These findings stress the direction of model design from traditional one-size-fits-all models to personalized models.</p><p> </p></div><h2>Other Information</h2> <p> Published in: User Modeling and User-Adapted Interaction<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="https://dx.doi.org/10.1007/s11257-023-09371-0" target="_blank">https://dx.doi.org/10.1007/s11257-023-09371-0</a></p>2023-06-17T03:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1007/s11257-023-09371-0https://figshare.com/articles/journal_contribution/One_size_does_not_fit_all_detecting_attention_in_children_with_autism_using_machine_learning/24934980CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/249349802023-06-17T03:00:00Z |
| spellingShingle | One size does not fit all: detecting attention in children with autism using machine learning Bilikis Banire (14158833) Education Education systems Information and computing sciences Human-centred computing Attention Autism Face-tracking Eye-tracking Machine learning |
| status_str | publishedVersion |
| title | One size does not fit all: detecting attention in children with autism using machine learning |
| title_full | One size does not fit all: detecting attention in children with autism using machine learning |
| title_fullStr | One size does not fit all: detecting attention in children with autism using machine learning |
| title_full_unstemmed | One size does not fit all: detecting attention in children with autism using machine learning |
| title_short | One size does not fit all: detecting attention in children with autism using machine learning |
| title_sort | One size does not fit all: detecting attention in children with autism using machine learning |
| topic | Education Education systems Information and computing sciences Human-centred computing Attention Autism Face-tracking Eye-tracking Machine learning |