[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120905-en":3,"doc-seo-120905-105":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},120905,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain - Sensors Article","Machine learning algorithms are pivotal for healthcare applications, yet regression methods for predicting changes in lifting movement patterns remain insufficiently evaluated. This pilot study applies regression-based machine learning to forecast alterations in trunk, hip, and knee motion following a 12-week strength training program for people with low back pain. A feature extraction approach computes sagittal-plane ranges of motion and compares 12 regression models. Ensemble Tree with LSBoost yields the highest accuracy for trunk prediction, while LSBoost also best predicts hip movement and Gaussian regression with an exponential kernel performs best for knee movement.","sensors   \nArticle  \nRegression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain  \nTrung C. Phan 1, Adrian Pranata 1,2,3,4, Joshua Farragher 3,4, Adam Bryant 5, Hung T. Nguyen 1 and Rifai Chai 1, *  \nCitation: Phan, T.C.; Pranata, A.; Farragher, J.; Bryant, A.; Nguyen, H.T.; Chai, R. Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain. Sensors 2024, 24, 1337. [https://doi.org/10.3390/](https://doi.org/10.3390/)[ ](https://doi.org/10.3390/)s24041337  \nAcademic Editor: Roozbeh Ghaffari  \n1 School of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, Australia; [tcphan@swin.edu.au](tcphan@swin.edu.au) (T.C.P.); [adrian.pranata@rmit.edu.au](adrian.pranata@rmit.edu.au) (A.P.); [hungnguyen@swin.edu.au](hungnguyen@swin.edu.au) (H.T.N.)  \n2 School of Health Sciences, Swinburne University of Technology, Hawthorn, VIC 3122, Australia  \n3 College of Rehabilitation Sciences, Shanghai University of Medicine and Health Sciences, Shanghai 201318, China; [joshua.farragher@rmit.edu.au](joshua.farragher@rmit.edu.au)  \n4 School of Health and Biomedical Sciences, RMIT University, Melbourne, VIC 3000, Australia  \n5 Centre for Health, Exercise and Sports Medicine, Department of Physiotherapy, The University of Melbourne, Melbourne, VIC 3010, Australia; [albryant@unimelb.edu.au](albryant@unimelb.edu.au)  \n* Correspondence: [rchai@swin.edu.au](rchai@swin.edu.au)  \nAbstract: Machine learning (ML) algorithms are crucial within the realm of healthcare applications. However, a comprehensive assessment of the effectiveness of regression algorithms in predicting alterations in lifting movement patterns has not been conducted. This research represents a pilot investigation using regression-based machine learning techniques to forecast alterations in trunk, hip, and knee movements subsequent to a 12-week strength training for people who have low back pain (LBP) . The system uses a feature extraction algorithm to calculate the range of motion in the sagittal plane for the knee, trunk, and hip and 12 different regression machine learning algorithms. The results show that Ensemble Tree with LSBoost demonstrated the utmost accuracy in prognosticating trunk movement. Meanwhile, the Ensemble Tree approach, specifically LSBoost, exhibited the highest predictive precision for hip movement. The Gaussian regression with the kernel chosen as exponential returned the highest prediction accuracy for knee movement. These regression models hold the potential to significantly enhance the precision of visualisation of the treatment output for individuals afflicted with LBP.  \nKeywords: low back pain; lifting technique; camera system; sagittal plane; trunk; hip; knee; range of motion; regression machine learning; forecast  \n1. Introduction  \nReceived: 14 January 2024  \nRevised: 8 February 2024  \nAccepted: 17 February 2024  \nPublished: 19 February 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \nLow back pain (LBP) is a common and debilitating condition affecting millions worldwide. Activities of daily living, such as repetitive lifting, have been associated with LBP. Lifting is an intricate task that necessitates coordination of the lower limbs (such as the hip and knee) as well as the trunk [1] . Poor lifting mechanics can occur for various reasons, such as lifting objects that are too heavy, lifting an object from an inappropriate height, lifting awkwardly shaped objects, or performing repetitive lifting tasks without proper rest and recovery [2–4] . Therefore, understanding and monitoring the changes i","cbCainb2EKeu2JHV","https://ap.wps.com/l/cbCainb2EKeu2JHV","pdf",3511695,1,17,"English","en",105,"# Introduction\n# Abstract\n# Keywords","[{\"question\":\"What is the goal of this regression-based machine learning study?\",\"answer\":\"To forecast changes in trunk, hip, and knee lifting movement patterns after a 12-week strength training intervention for people with low back pain.\"},{\"question\":\"How is movement data represented and used in the models?\",\"answer\":\"The study uses feature extraction to calculate sagittal-plane range of motion for the knee, trunk, and hip, then applies 12 different regression machine learning algorithms.\"},{\"question\":\"Which regression models performed best for each body region?\",\"answer\":\"Ensemble Tree with LSBoost is best for trunk prediction, LSBoost best for hip prediction, and Gaussian regression with an exponential kernel is best for knee prediction.\"}]","Regression-Based Machine Learning for Predicting Lifting Movement Pattern Change in People with Low Back Pain - Sensors Article | PDF",1785732604,43,{"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},"regression-based-machine-learning-for-predicting-lifting-movement-pattern-change-in-people-with-low-back-pain-sensors-article","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/regression-based-machine-learning-for-predicting-lifting-movement-pattern-change-in-people-with-low-back-pain-sensors-article/120905/",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","2026-08-03",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 goal of this regression-based machine learning study?","Question",{"text":75,"@type":76},"To forecast changes in trunk, hip, and knee lifting movement patterns after a 12-week strength training intervention for people with low back pain.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is movement data represented and used in the models?",{"text":80,"@type":76},"The study uses feature extraction to calculate sagittal-plane range of motion for the knee, trunk, and hip, then applies 12 different regression machine learning algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"Which regression models performed best for each body region?",{"text":84,"@type":76},"Ensemble Tree with LSBoost is best for trunk prediction, LSBoost best for hip prediction, and Gaussian regression with an exponential kernel is best for knee prediction.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]