Perspective Chapter: Leveraging Machine Learning for Early Detection of Autism Spectrum Disorder through Motion Analysis
<p id="p1">Autism spectrum disorder (ASD) is traditionally diagnosed through extended behavioral observations. New evidence suggests that motor abnormalities may underline the disorder and serve as computational markers, potentially improving both assessment and diagnostic processes. In this context...
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| Autores principales: | , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2025
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| Acceso en línea: | https://www.intechopen.com/chapters/1215321 |
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| _version_ | 1879164877258358784 |
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| author | Simeoli, Roberta Marocco, Davide |
| author_browse | Marocco, Davide Simeoli, Roberta |
| author_facet | Simeoli, Roberta Marocco, Davide |
| author_sort | Simeoli, Roberta |
| building | IntechOpen |
| collection | Acceso abierto |
| description | <p id="p1">Autism spectrum disorder (ASD) is traditionally diagnosed through extended behavioral observations. New evidence suggests that motor abnormalities may underline the disorder and serve as computational markers, potentially improving both assessment and diagnostic processes. In this context, machine learning (ML)-based approaches are paving the way for faster, more objective methods of early ASD detection by analyzing these transparent, measurable indicators. This chapter provides an overview of theories supporting these new assessment perspectives and recent ML applications that can assist ASD diagnostic processes through the analysis of motor data collected <em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">via</italic></em> digital devices, such as tablets with touchscreen sensors or wearable sensors. Offering a comprehensive and critical view of these innovations, this chapter highlights the potential contributions of ML-based motor analysis in detecting autism biomarkers, the implications for early diagnosis, and the ongoing challenges in integrating these tools into clinical practice.</p> |
| doi_str_mv | 10.5772/intechopen.1009901 |
| first_indexed | 2026-10-15T21:41:19Z |
| format | Book Chapter |
| fullrecord | <oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd"><identifier>intech-1215321</identifier><datestamp>2025-05-13</datestamp>
<dc:title>Perspective Chapter: Leveraging Machine Learning for Early Detection of Autism Spectrum Disorder through Motion Analysis</dc:title>
<dc:creator>Roberta Simeoli</dc:creator>
<dc:creator>Davide Marocco</dc:creator>
<dc:subject>Health Sciences</dc:subject>
<dc:description><p id="p1">Autism spectrum disorder (ASD) is traditionally diagnosed through extended behavioral observations. New evidence suggests that motor abnormalities may underline the disorder and serve as computational markers, potentially improving both assessment and diagnostic processes. In this context, machine learning (ML)-based approaches are paving the way for faster, more objective methods of early ASD detection by analyzing these transparent, measurable indicators. This chapter provides an overview of theories supporting these new assessment perspectives and recent ML applications that can assist ASD diagnostic processes through the analysis of motor data collected <em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">via</italic></em> digital devices, such as tablets with touchscreen sensors or wearable sensors. Offering a comprehensive and critical view of these innovations, this chapter highlights the potential contributions of ML-based motor analysis in detecting autism biomarkers, the implications for early diagnosis, and the ongoing challenges in integrating these tools into clinical practice.</p></dc:description>
<dc:publisher>IntechOpen</dc:publisher>
<dc:date>2025-05-13</dc:date>
<dc:type>Chapter, Part Of Book</dc:type>
<dc:identifier>https://www.intechopen.com/chapters/1215321</dc:identifier>
<dc:identifier>doi:10.5772/intechopen.1009901</dc:identifier>
<dc:language>en</dc:language>
<dc:relation>ISBN:978-1-83634-839-9</dc:relation>
<dc:rights>https://creativecommons.org/licenses/by/3.0/</dc:rights>
<dc:source>https://www.intechopen.com/books/1004511 ; Bridging Biology and Behavior in Autism - Innovations in Research and Practice</dc:source>
</oai_dc:dc> |
| id | intech-1215321 |
| institution | IntechOpen |
| isbn | 978-1-83634-839-9 |
| language | English |
| last_indexed | 2026-10-15T21:41:19Z |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | IntechOpen |
| publisherStr | IntechOpen |
| record_format | oai_dc |
| spelling | intech-12153212025-05-13 Perspective Chapter: Leveraging Machine Learning for Early Detection of Autism Spectrum Disorder through Motion Analysis Roberta Simeoli Davide Marocco Health Sciences <p id="p1">Autism spectrum disorder (ASD) is traditionally diagnosed through extended behavioral observations. New evidence suggests that motor abnormalities may underline the disorder and serve as computational markers, potentially improving both assessment and diagnostic processes. In this context, machine learning (ML)-based approaches are paving the way for faster, more objective methods of early ASD detection by analyzing these transparent, measurable indicators. This chapter provides an overview of theories supporting these new assessment perspectives and recent ML applications that can assist ASD diagnostic processes through the analysis of motor data collected <em><italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">via</italic></em> digital devices, such as tablets with touchscreen sensors or wearable sensors. Offering a comprehensive and critical view of these innovations, this chapter highlights the potential contributions of ML-based motor analysis in detecting autism biomarkers, the implications for early diagnosis, and the ongoing challenges in integrating these tools into clinical practice.</p> IntechOpen 2025-05-13 Chapter, Part Of Book https://www.intechopen.com/chapters/1215321 doi:10.5772/intechopen.1009901 en ISBN:978-1-83634-839-9 https://creativecommons.org/licenses/by/3.0/ https://www.intechopen.com/books/1004511 ; Bridging Biology and Behavior in Autism - Innovations in Research and Practice |
| spellingShingle | Health Sciences Simeoli, Roberta Marocco, Davide Perspective Chapter: Leveraging Machine Learning for Early Detection of Autism Spectrum Disorder through Motion Analysis |
| title | Perspective Chapter: Leveraging Machine Learning for Early Detection of Autism Spectrum Disorder through Motion Analysis |
| title_full | Perspective Chapter: Leveraging Machine Learning for Early Detection of Autism Spectrum Disorder through Motion Analysis |
| title_fullStr | Perspective Chapter: Leveraging Machine Learning for Early Detection of Autism Spectrum Disorder through Motion Analysis |
| title_full_unstemmed | Perspective Chapter: Leveraging Machine Learning for Early Detection of Autism Spectrum Disorder through Motion Analysis |
| title_short | Perspective Chapter: Leveraging Machine Learning for Early Detection of Autism Spectrum Disorder through Motion Analysis |
| title_sort | perspective chapter leveraging machine learning for early detection of autism spectrum disorder through motion analysis |
| topic | Health Sciences |
| url | https://www.intechopen.com/chapters/1215321 |
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