Autoencoder-Based Data Clustering for Identifying Anomalous Repetitive Hand Movements, and Behavioral Transition Patterns in Children

Autoencoder-Based Data Clustering for Identifying Anomalous Repetitive Hand Movements, and Behavioral Transition Patterns in Children | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Autoencoder-Based Data Clustering for Identifying Anomalous Repetitive Hand Movements, and Behavioral Transition Patterns in Children Nushara Deshith Wedasingha, Pradeepa Samarasinghe, Lasantha Senevirathe, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4363669/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 21 Jan, 2025 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted 6 You are reading this latest preprint version Abstract The analysis of repetitive hand movements and behavioral transition patterns holds particular significance in detecting atypical behaviors in early child development. Early recognition of these behaviors holds immense promise for timely interventions, which can profoundly impact a child's well-being and future prospects. However, the scarcity of specialized medical professionals and limited facilities has made detecting these behaviors and unique patterns challenging using traditional manual methods. This highlights the necessity for automated tools to identify anomalous repetitive hand movements and behavioral transition patterns in children. Our study aimed to develop an automated model for the early identification of anomalous repetitive hand movements and the detection of unique behavioral patterns. Utilizing autoencoders, self-similarity matrices, and unsupervised clustering algorithms, we analyzed skeleton and image-based features, repetition count, and frequency of repetitive child hand movements. This approach aimed to distinguish between typical and atypical repetitive hand movements of varying speeds, addressing data limitations through dimension reduction. Additionally, we aimed to categorize behaviors into clusters beyond binary classification. Through experimentation on three datasets (HMW, SSBD, ASD), our model effectively differentiated between typical and atypical hand movements, providing insights into behavioral transitional patterns. This aids the medical community in understanding the evolving behaviors in children. In conclusion, our research addresses the need for early detection of atypical behaviors through an automated model capable of discerning repetitive hand movement patterns. This innovation contributes to early intervention strategies for neurological conditions. Repetitive Hand Movements Atypical Behaviors Auto-Encoders Data Clustering K-means Movement Analysis Dimension Reduction Pattern Analysis Unsupervised Learning Repetition Counting Repetition Frequency Full Text Cite Share Download PDF Status: Published Journal Publication published 21 Jan, 2025 Read the published version in Physical and Engineering Sciences in Medicine → Version 1 posted Reviewers agreed at journal 09 Jun, 2024 Reviewers invited by journal 31 May, 2024 Editor invited by journal 21 May, 2024 Editor assigned by journal 10 May, 2024 First submitted to journal 10 May, 2024 Editorial decision: Minor revisions 07 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4363669","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":309128313,"identity":"daea6faa-061b-405e-b3c8-680cc68a75a2","order_by":0,"name":"Nushara Deshith Wedasingha","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYHACNjBpf7wBSBpYkKCF4cwBkBYJUrTcSACRRGgxl0h/9uDnHrvExpnPr274USDBwN/enYBXi+WMHHPDnmfJic3SOWU3e4AOkzhzdgNeLQY3ctgkeA4w57ZJ56Td4AFqMZDIJaQl/ZnknwP1uT2SZ9Ju/iFOS4KZNM+Bw7kzJNiP3SbOljNvzKRlDhyv38CTw3ZbxkCCh7BfjgMd9uZAtbEB+/FnN9/8sZHjb+/FrwUJ8BiASWKVgwD7A1JUj4JRMApGwQgCAGzMSNnQLU94AAAAAElFTkSuQmCC","orcid":"","institution":"Sri Lanka Institute of Information Technology - 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