[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126661-en":3,"doc-seo-126661-105":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},126661,549768064778,"Finn","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis","This study develops and validates machine learning models to predict physical activity intensity in children and adolescents with cystic fibrosis across different accelerometer brands and sensor placements. Participants with cystic fibrosis and healthy youth completed six activities while wearing GENEActiv (both wrists) and ActiGraph GT9X (both wrists and waist). Three supervised classifiers were trained using accelerometer signal patterns and evaluated with 10-fold cross-validation. Most models achieved over 97% accuracy with sensitivity and specificity above 95%, largely independent of device brand, placement, or health condition.","PR IFYS GOL BANGOR / BANGOR  \nA Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis  \nSilveira Bianchim, Mayara; McNarry, Melitta A. ; Barker, Alan; Williams, Craig; Denford, Sarah; Thia, Lena; Evans, Rachel ; Mackintosh, Kelly  \nMeasurement in Physical Education and Exercise Science  \nDOI:  \n[https://doi.org/10.1080/1091367X.2023.2271444](https://doi.org/10.1080/1091367X.2023.2271444)  \n[E-pub ahead of print: 24/10/2023](E-pub ahead of print: 24/10/2023)  \nPublisher's PDF, also known as Version of record  \nCyswllt i'r cyhoeddiad / Link to publication  \nDyfyniad o'r fersiwn a gyhoeddwyd / Citation for published version (APA):  \nSilveira Bianchim, M. , McNarry, M. A. , Barker, A. , Williams, C. , Denford, S. , Thia, L. , Evans, R. ,& Mackintosh, K. (2023) . A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis. Measurement in Physical Education and Exercise Science. [https://doi.org/10.1080/1091367X.2023.2271444](https://doi.org/10.1080/1091367X.2023.2271444)  \nHawliau Cyffredinol / General rights  \nCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.  \n• Users may download and print one copy of any publication from the public portal for the purpose of private study or research.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying the publication in the public portal ?  \nTake down policy  \nIf you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.  \nMeasurement in Physical Education and Exercise Science  \nISSN: (Print) (Online) Journal homepage: [https://www.tandfonline.com/loi/hmpe20](https://www.tandfonline.com/loi/hmpe20)  \nA Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis  \nMayara S. Bianchim, Melitta A. McNarry, Alan R. Barker, Craig A. Williams, Sarah Denford, Lena Thia, Rachel Evans & Kelly A Mackintoshon behalf of ActiveYouth SRC group  \nTo cite this article: Mayara S. Bianchim, Melitta A. McNarry, Alan R. Barker, Craig A. Williams, Sarah Denford, Lena Thia, Rachel Evans & Kelly A Mackintoshon behalf of ActiveYouth SRC group (24 Oct 2023): A Machine Learning Approach for Physical Activity  \nRecognition in Cystic Fibrosis, Measurement in Physical Education and Exercise Science, DOI: 10. 1080/1091367X.2023.2271444  \nTo link to this article: [https://doi.org/10.1080/1091367X.2023.2271444](https://doi.org/10.1080/1091367X.2023.2271444)  \n© 2023 The Author(s) . Published with license by Taylor & Francis Group, LLC.  \n\n|  View supplementary material  |\n| --- |\n|  Published online: 24 Oct 2023. |\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=hmpe20](https://www.tandfonline.com/action/journalInformation?journalCode=hmpe20)  \nMEASUREMENT IN PHYSICAL EDUCATION AND EXERCISE SCIENCE [https://doi.org/10.1080/1091367X.2023.2271444](https://doi.org/10.1080/1091367X.2023.2271444)  \nA Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis  \nMayara S. Bianchima,b, Melitta A. McNarry a, Alan R. Barkerc, Craig A. Williamsc, Sarah Denfordc, Lena Thiad, Rachel Evanse, Kelly A Mackintosh a, and on behalf of ActiveYouth SRC group  \naApplied Sports, Technology, Exercise and Medicine Research Centre, Swansea University, Swansea, UK; bSchool of Medical and Health Sciences, Bangor University, Bangor, UK; cChildren’s Health and Exercise Research Centre, University of Exeter, Exeter, UK; dDepartment of Paediatric Respiratory Medicine and Cy","cbCaiqPTSjGNZGeh","https://ap.wps.com/l/cbCaiqPTSjGNZGeh","pdf",973438,1,12,"English","en",105,"# Abstract\n# Introduction\n## Cystic fibrosis and exercise limitations\n## Physical activity benefits in youth with cystic fibrosis\n## Accelerometer cut-points and limitations","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To develop and validate machine learning models that predict physical activity intensity in children and adolescents with cystic fibrosis, using data from different accelerometer brands and placements.\"},{\"question\":\"Which activity-recognition methods/classifiers were used?\",\"answer\":\"K-Nearest Neighbour, Random Forest, and an eXtreme Gradient Boosted Decision Tree were used, trained on accelerometer signal patterns for each activity type and intensity.\"},{\"question\":\"How accurate were the models, and what failed to predict vigorous activity?\",\"answer\":\"Most models for activity type and intensity exceeded 97% accuracy with sensitivity and specificity above 95%. ActiGraph GT9X on the dominant wrist and waist and GENEActiv on the dominant wrist failed to predict vigorous intensity activity.\"}]","A Machine Learning Approach for Physical Activity Recognition in Cystic Fibrosis | PDF",1785934088,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-approach-for-physical-activity-recognition-in-cystic-fibrosis","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-approach-for-physical-activity-recognition-in-cystic-fibrosis/126661/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To develop and validate machine learning models that predict physical activity intensity in children and adolescents with cystic fibrosis, using data from different accelerometer brands and placements.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which activity-recognition methods/classifiers were used?",{"text":80,"@type":76},"K-Nearest Neighbour, Random Forest, and an eXtreme Gradient Boosted Decision Tree were used, trained on accelerometer signal patterns for each activity type and intensity.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate were the models, and what failed to predict vigorous activity?",{"text":84,"@type":76},"Most models for activity type and intensity exceeded 97% accuracy with sensitivity and specificity above 95%. ActiGraph GT9X on the dominant wrist and waist and GENEActiv on the dominant wrist failed to predict vigorous intensity activity.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":29,"slug":121},"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]