DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms
IntroductionThe early detection and diagnosis of autism spectrum disorder (ASD) remain critical challenges in developmental healthcare, with traditional diagnostic methods relying heavily on subjective clinical observations.MethodsIn this paper, we introduce an innovative multi-stream framework that...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Physiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fphys.2025.1593965/full |
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| author | Theyazn H. H. Aldhyani Theyazn H. H. Aldhyani Abdullah H. Al-Nefaie Abdullah H. Al-Nefaie |
| author_facet | Theyazn H. H. Aldhyani Theyazn H. H. Aldhyani Abdullah H. Al-Nefaie Abdullah H. Al-Nefaie |
| author_sort | Theyazn H. H. Aldhyani |
| collection | DOAJ |
| description | IntroductionThe early detection and diagnosis of autism spectrum disorder (ASD) remain critical challenges in developmental healthcare, with traditional diagnostic methods relying heavily on subjective clinical observations.MethodsIn this paper, we introduce an innovative multi-stream framework that seamlessly integrates three state-of-the-art convolutional neural networks, namely, EfficientNetV2B0, ResNet50V2, DenseNet121, and Multi-Stream models to analyze stereotypical movements, particularly hand-flapping behaviors automatically. Our architecture incorporates sophisticated spatial and temporal attention mechanisms enhanced by hierarchical feature fusion and adaptive temporal sampling techniques designed to extract characteristics of ASD related movements across multiple scales. The system includes a custom designed temporal attention module that effectively captures the rhythmic nature of hand-flapping behaviors. The spatial attention mechanisms method was used to enhance the proposed models by focusing on the movement characteristics of the patients in the video. The experimental validation was conducted using the Self-Stimulatory Behavior Dataset (SSBD), which includes 66 videos.ResultsThe Multi-Stream framework demonstrated exceptional performance, with 96.55% overall accuracy, 100% specificity, and 94.12% sensitivity in terms of hand-flapping detection and an impressive F1 score of 97%.DiscussionThis research can provide healthcare professionals with a reliable, automated tool for early ASD screening that offers objective, quantifiable metrics that complement traditional diagnostic methods. |
| format | Article |
| id | doaj-art-bd0634b048524194b472c80d23aae898 |
| institution | DOAJ |
| issn | 1664-042X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Physiology |
| spelling | doaj-art-bd0634b048524194b472c80d23aae8982025-08-20T02:43:33ZengFrontiers Media S.A.Frontiers in Physiology1664-042X2025-07-011610.3389/fphys.2025.15939651593965DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanismsTheyazn H. H. Aldhyani0Theyazn H. H. Aldhyani1Abdullah H. Al-Nefaie2Abdullah H. Al-Nefaie3King Salman Center for Disability Research, Riyadh, Saudi ArabiaApplied college in Abqaiq, King Faisal University, Al-Ahsa, Saudi ArabiaKing Salman Center for Disability Research, Riyadh, Saudi ArabiaDepartment of Quantitative Methods, School of Business, King Faisal University, Al-Ahsa, Saudi ArabiaIntroductionThe early detection and diagnosis of autism spectrum disorder (ASD) remain critical challenges in developmental healthcare, with traditional diagnostic methods relying heavily on subjective clinical observations.MethodsIn this paper, we introduce an innovative multi-stream framework that seamlessly integrates three state-of-the-art convolutional neural networks, namely, EfficientNetV2B0, ResNet50V2, DenseNet121, and Multi-Stream models to analyze stereotypical movements, particularly hand-flapping behaviors automatically. Our architecture incorporates sophisticated spatial and temporal attention mechanisms enhanced by hierarchical feature fusion and adaptive temporal sampling techniques designed to extract characteristics of ASD related movements across multiple scales. The system includes a custom designed temporal attention module that effectively captures the rhythmic nature of hand-flapping behaviors. The spatial attention mechanisms method was used to enhance the proposed models by focusing on the movement characteristics of the patients in the video. The experimental validation was conducted using the Self-Stimulatory Behavior Dataset (SSBD), which includes 66 videos.ResultsThe Multi-Stream framework demonstrated exceptional performance, with 96.55% overall accuracy, 100% specificity, and 94.12% sensitivity in terms of hand-flapping detection and an impressive F1 score of 97%.DiscussionThis research can provide healthcare professionals with a reliable, automated tool for early ASD screening that offers objective, quantifiable metrics that complement traditional diagnostic methods.https://www.frontiersin.org/articles/10.3389/fphys.2025.1593965/fullautism spectrum disorderdeep learningstereotypical movementshandflapping detectionmulti-stream architectureattention mechanisms |
| spellingShingle | Theyazn H. H. Aldhyani Theyazn H. H. Aldhyani Abdullah H. Al-Nefaie Abdullah H. Al-Nefaie DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms Frontiers in Physiology autism spectrum disorder deep learning stereotypical movements handflapping detection multi-stream architecture attention mechanisms |
| title | DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms |
| title_full | DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms |
| title_fullStr | DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms |
| title_full_unstemmed | DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms |
| title_short | DASD- diagnosing autism spectrum disorder based on stereotypical hand-flapping movements using multi-stream neural networks and attention mechanisms |
| title_sort | dasd diagnosing autism spectrum disorder based on stereotypical hand flapping movements using multi stream neural networks and attention mechanisms |
| topic | autism spectrum disorder deep learning stereotypical movements handflapping detection multi-stream architecture attention mechanisms |
| url | https://www.frontiersin.org/articles/10.3389/fphys.2025.1593965/full |
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