[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121741-en":3,"doc-seo-121741-105":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},121741,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Novel computational protocol to support transfemoral prosthetic alignment procedure using machine learning techniques - Journal Article","Prosthetic alignment for transfemoral amputees uses biomechanical, anatomical, and comfort considerations to achieve acceptable gait, yet malalignment can trigger long-term musculoskeletal disease. Because alignment assessment is variable and strongly subjective to prosthetist experience, the study introduces a machine learning–based computational protocol to assist judgment. Sixteen amputees were used for training and validation with ground reaction force parameters, and SVM and Bayesian neural networks predicted nominal alignment and correction angle and magnitude, showing strong agreement with clinicians.","Gait & Posture 102 (2023) 125–131  \nContents lists available at ScienceDirect  \nGait & Posture  \njournal [homepage: www.elsevier.com/locate/gaitpost](homepage: www.elsevier.com/locate/gaitpost)  \n| Novel computational protocol to support transfemoral prosthetic alignment   procedure using machine learning techniques\u003Cbr>Andres M. C´ardenasa, b, *, Juliana Uribe a, Josep M. Font-Llagunesc, d, Alher M. Hern´andez a, Jesús A. Plata e, f\u003Cbr>a Bioinstrumentation and Clinical Engineering Research Group – GIBIC, Bioengineering Department, Engineering Faculty, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia\u003Cbr>b Research Group in Computational Modeling and Simulation – GIMSC, Engineering Faculty, Universidad de San Buenaventura, Carrera 56C No. 51-110, Medellín, Colombia\u003Cbr>c Biomechanical Engineering Lab, Department of Mechanical Engineering and Research Centre for Biomedical Engineering, Universitat Polit`ecnica de Catalunya, Diagonal\u003Cbr>647, 08028 Barcelona, Spain\u003Cbr>d Institut de Recerca Sant Joan de D´eu, Santa Rosa 39‑57, 08950 Esplugues de Llobregat, Spain\u003Cbr>e Grupo Rehabilitaci´on en Salud, Sede de Investigaci´on Universitaria, Universidad de Antioquia UdeA, Calle 70 No. 52-21, Medellín, Colombia f Mahavir Kmina Artificial Limb Center, Carrera 54 No. 79 AA Sur 40, Bodegas La Troja, Local 116, La Estrella, Colombia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Prosthetic alignment Transfemoral amputees Ground reaction force Neural networks Support Vector Machine |  | Background: The prosthetic alignment procedure considers biomechanical, anatomical and comfort characteristics of the amputee to achieve an acceptable gait. Prosthetic malalignment induces long-term disease. The assessment of alignment is highly variable and subjective to the experience of the prosthetist, so the use of machine learning could assist the prosthetist during the judgment of optimal alignment.\u003Cbr>Research objective: To assist the prosthetist during the assessment of prosthetic alignment using a new computational protocol based on machine learning.\u003Cbr>Methods: Sixteen transfemoral amputees were recruited for training and validation of the alignment protocol. Four misalignments and one nominal alignment were performed. Eleven prosthetic limb ground reaction force parameters were recorded. A support vector machine with a Gaussian kernel radial basis function and a Bayesian regularization neural network were trained to predict the alignment condition, as well as the magnitude and angle of required to align the prosthesis correctly. The alignment protocol was validated by one junior and one senior prosthetist during the prosthetic alignment of two transfemoral amputees.\u003Cbr>Results: The support vector machine-based model detected the nominal alignment 92.6 % of the time. The neural network recovered 94.11 % of the angles needed to correct the prosthetic misalignment with a fitting error of 0.51◦ . During the validation of the alignment protocol, the computational models and the prosthetists agreed on the alignment assessment. The gait quality evaluated by the prosthetists reached a satisfaction level of 8/10 for the first amputee and 9.6/10 for the second amputee.\u003Cbr>Importance: The new computational prosthetic alignment protocol is a tool that helps the prosthetist during the prosthetic alignment procedure thereby decreasing the likelihood of gait deviations and musculoskeletal diseases associated with misalignments and consequently improving the amputees-prosthesis adherence. |\n\n1. Introduction  \nThe goal of prosthetic alignment of the lower extremity is to match the prosthetic load lines with the anatomical and biomechanical ones of the amputee [1], to provide stability, an efficient gait, and movement functionality [2]. Prosthetic mismatches induce gait deviations that  \nresult in injury to the musculoskeletal system and affect the amputee’s quality of life [3]. To avoid prosthetic misalig","cbCaidoFaTEyuWp5","https://ap.wps.com/l/cbCaidoFaTEyuWp5","pdf",2656908,1,7,"English","en",105,"# Article info\n## Keywords\n# Introduction\n## Prosthetic alignment goals and risks\n## Prior computational tools\n# Research objective and methods\n## Participants and recorded parameters\n## Machine learning models and training\n## Validation procedure\n# Results and importance\n## Model detection and angle recovery\n## Clinician agreement and satisfaction","[{\"question\":\"Why is prosthetic alignment assessment important for transfemoral amputees?\",\"answer\":\"Prosthetic alignment aims to match prosthetic load lines with anatomical and biomechanical ones to enable stable, efficient gait. Misalignment can cause gait deviations and long-term musculoskeletal injury, reducing quality of life.\"},{\"question\":\"What machine learning models were used in the proposed alignment protocol?\",\"answer\":\"A support vector machine with a Gaussian kernel radial basis function and a Bayesian regularization neural network were trained using ground reaction force parameters. They predict alignment condition and the magnitude and angle needed for correct alignment.\"},{\"question\":\"How was the protocol validated and what outcomes were reported?\",\"answer\":\"The protocol was validated by one junior and one senior prosthetist during prosthetic alignment of two transfemoral amputees. The SVM detected nominal alignment 92.6% of the time, the neural network recovered correction angles with 0.51° fitting error, and clinicians agreed on alignment assessment while reporting high satisfaction scores.\"}]","Novel computational protocol to support transfemoral prosthetic alignment procedure using machine learning techniques - Journal Article | PDF",1785806589,18,{"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},"novel-computational-protocol-to-support-transfemoral-prosthetic-alignment-procedure-using-machine-learning-techniques-journal-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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/novel-computational-protocol-to-support-transfemoral-prosthetic-alignment-procedure-using-machine-learning-techniques-journal-article/121741/",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","2026-08-04",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},"Why is prosthetic alignment assessment important for transfemoral amputees?","Question",{"text":75,"@type":76},"Prosthetic alignment aims to match prosthetic load lines with anatomical and biomechanical ones to enable stable, efficient gait. Misalignment can cause gait deviations and long-term musculoskeletal injury, reducing quality of life.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What machine learning models were used in the proposed alignment protocol?",{"text":80,"@type":76},"A support vector machine with a Gaussian kernel radial basis function and a Bayesian regularization neural network were trained using ground reaction force parameters. They predict alignment condition and the magnitude and angle needed for correct alignment.",{"name":82,"@type":73,"acceptedAnswer":83},"How was the protocol validated and what outcomes were reported?",{"text":84,"@type":76},"The protocol was validated by one junior and one senior prosthetist during prosthetic alignment of two transfemoral amputees. 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