[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124395-en":3,"doc-seo-124395-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},124395,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning","ACL reconstruction rates are rising, especially among female athletes, but the underlying causes and biomechanical mechanisms are not fully understood. This study builds and evaluates machine learning models to predict ACL length, strain, and tensile force during six high-impact and daily activities. It examines the importance of kinematic and constitutional parameters and characterizes gender-related injury risk patterns, using 9,375 observations per variable across 42 trained models. Results highlight strong predictors and higher rotation-related strain in females.","Please cite the Published Version  \nRoldán Ciudad, Elisa, Reeves, Neil D. , Cooper, Glen and Andrews, Kirstie  (2025) Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning. Computer Methods in Biomechanics and Biomedical Engineering. ISSN 1025-5842  \nDOI: [https://doi.org/10.1080/10255842.2025.2551846](https://doi.org/10.1080/10255842.2025.2551846)  \nPublisher: Taylor & Francis  \nVersion: Published Version  \nDownloaded from: [https://e-space.mmu.ac.uk/641611/](https://e-space.mmu.ac.uk/641611/)  \nUsage rights:  Creative Commons: Attribution 4 .0  \nAdditional Information: This is an open access article published in Computer Methods in Biomechanics and Biomedical Engineering, by Taylor & Francis.  \nData Access Statement: All data supporting this article is provided in the manuscript and in the supplementary materials.  \nEnquiries:  \nIf you have questions about this document, contact [openresearch@mmu.ac.uk. Please](openresearch@mmu.ac.uk. Please) include the URL of the record in e-space. If you believe that your, or a third party’s rights have been compromised through this document please see our Take Down policy (available from [https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines](https://www.mmu.ac.uk/library/using-the-library/policies-and-guidelines))  \nComputer Methods in Engineering  \nBiomechanics and  \nBiomedical  \nISSN: 1025-5842 (Print) 1476-8259 (Online) Journal [homepage: ](homepage: www.tandfonline.com/journals/gcmb20)[www.tandfonline.com/journals/gcmb20](homepage: www.tandfonline.com/journals/gcmb20)  \nInvestigating ACL length, strain and tensile force in high impact and daily activities through machine learning  \nElisa Roldán Ciudad , Neil D. Reeves , Glen Cooper & Kirstie Andrews  \nTo cite this article: Elisa Roldán Ciudad , Neil D. Reeves , Glen Cooper & Kirstie Andrews (30 Aug 2025): Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning, Computer Methods in Biomechanics and Biomedical Engineering, DOI: 10. 1080/10255842 .2025.2551846  \nTo link to this article: [https://doi.org/10.1080/10255842.2025.2551846](https://doi.org/10.1080/10255842.2025.2551846)  \n© 2025 The Author(s) . Published by Informa UK Limited, trading as Taylor & Francis Group  \n\n|  View supplementary material  |  |\n| --- | --- |\n|  Published online: 30 Aug 2025. |  |\n|  | Submit your article to this journal  |\n|  | View related articles  |\n|  View Crossmark data |  |\n\nFull Terms & Conditions of access and use can be found at [https://www.tandfonline.com/action/journalInformation?journalCode=gcmb20](https://www.tandfonline.com/action/journalInformation?journalCode=gcmb20)  \nCOMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING [https://doi.org/10.1080/10255842.2025.2551846](https://doi.org/10.1080/10255842.2025.2551846)  \nInvestigating ACL length, strain and tensile force in high impact and daily activities through machine learning  \nElisa Roldn Ciudada, Neil D. Reevesb, Glen Cooperc and Kirstie Andrewsa  \naDepartment of Engineering, Faculty of Science and Engineering, Manchester Metropolitan University, Manchester, UK; bLancaster Medical School, Faculty of Health and Medicine, Lancaster University, Lancaster, UK; cSchool of Engineering, University of Manchester, Manchester, UK  \nABSTRACT  \nAnterior cruciate ligament (ACL) reconstruction rates are rising, particularly among female athletes, though causes remain unclear. This study: (i) identify accurate machine learning models to predict ACL length, strain, and force during six high-impact and daily activities; (ii) assess the significance of kinematic and constitutional parameters; and (iii) analyse gender-based injury risk patterns. Using 9,375 observations per variable, 42 models were trained. Cubist, Generalized Boosted Models (GBM), and Random Forest (RF) achieved the best R2, RMSE, and MAE. Knee flexion and external rotation strongly predicted ACL str","cbCaiuIia6oUtzlX","https://ap.wps.com/l/cbCaiuIia6oUtzlX","pdf",3024631,1,17,"English","en",105,"# Abstract\n# Introduction\n## ACL injury burden and reconstruction trends\n## Role of ACL in knee stability\n## Gender disparity and unclear mechanisms\n# Methods and Modeling\n## Machine learning model training and evaluation\n# Results\n## Key predictors of ACL strain and tensile force\n## Gender-based risk patterns\n# Keywords","[{\"question\":\"What does the study aim to predict about the ACL?\",\"answer\":\"It predicts ACL length, strain, and tensile force during six high-impact and daily activities using machine learning.\"},{\"question\":\"How were the machine learning models evaluated?\",\"answer\":\"Forty-two models were trained using 9,375 observations per variable, and performance was assessed with metrics including R2, RMSE, and MAE.\"},{\"question\":\"Which biomechanical factors were most influential in predicting ACL strain and force?\",\"answer\":\"Knee flexion and external rotation strongly predicted ACL strain and tensile force.\"}]","Investigating ACL length, strain and tensile force in high impact and daily activities through machine learning | 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