[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-seo-290389-105":3,"detail-sidebar-cat-0-en-105":81,"doc-detail-290389-en":130},{"code":4,"msg":5,"data":6},0,"ok",{"site_id":7,"language":8,"slug":9,"title":10,"keywords":11,"description":12,"schema_data":13,"social_meta":74,"head_meta":76,"extra_data":78,"updated_unix":80},105,"en","machine-learning-for-enhanced-robotic-assisted-stroke-assessment-doctor-of-philosophy-thesis","Machine Learning For Enhanced Robotic-Assisted Stroke Assessment - Doctor of Philosophy Thesis","","Stroke is a leading cause of long-term adult disability worldwide, demanding neurological assessment methods that are precise, objective, and sensitive enough to guide therapy. Conventional assessments depend largely on clinician observation, which is subjective and may not provide the granularity needed for personalized rehabilitation. This thesis uses machine learning with Kinarm exoskeleton robotic data to address gaps in robotic-data learning, normative one-class models, label ambiguity, and lengthy assessment protocols.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/machine-learning-for-enhanced-robotic-assisted-stroke-assessment-doctor-of-philosophy-thesis/290389/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/machine-learning-for-enhanced-robotic-assisted-stroke-assessment-doctor-of-philosophy-thesis/290389.png","ImageObject",300,407,{"name":42,"@type":43},"Eliana","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-11-02","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",10,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What problem does the thesis address in stroke assessment?","Question",{"text":63,"@type":64},"It addresses the need for more precise and objective neurological assessment than clinician observations, which are subjective and may lack sufficient detail for personalized rehabilitation.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How does the thesis use robotic data in its machine learning approach?",{"text":68,"@type":64},"It leverages Kinarm Exoskeleton Lab kinematic data and applies machine learning to distinguish stroke from control participants and to extract fine-grained impairment patterns.",{"name":70,"@type":61,"acceptedAnswer":71},"What strategies are proposed to improve classification sensitivity and reduce assessment time?",{"text":72,"@type":64},"The thesis introduces uncertainty-aware techniques to better handle ambiguous data and uses foundation models for time series forecasting to predict additional trials from limited initial data, reducing required assessment duration.","https://schema.org",{"og:url":32,"og:type":75,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":77,"canonical":32},"index,follow",{"doc_id":79,"site_id":7},290389,1789641545,{"code":4,"msg":82,"data":83},"success",[84,88,92,96,101,106,111,115,120,123,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":85,"show_sort_weight":86,"slug":87},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":89,"show_sort_weight":90,"slug":91},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":93,"show_sort_weight":94,"slug":95},"Exam",70,"exam",{"id":97,"doc_module":4,"doc_module_name":25,"category_name":98,"show_sort_weight":99,"slug":100},5,"Comic",60,"comic",{"id":102,"doc_module":4,"doc_module_name":25,"category_name":103,"show_sort_weight":104,"slug":105},6,"Technology",50,"technology",{"id":107,"doc_module":4,"doc_module_name":25,"category_name":108,"show_sort_weight":109,"slug":110},7,"Healthcare",40,"healthcare",{"id":112,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":113,"slug":114},8,30,"research-report",{"id":116,"doc_module":4,"doc_module_name":25,"category_name":117,"show_sort_weight":118,"slug":119},9,"Religion & Spirituality",20,"religion-spirituality",{"id":118,"doc_module":4,"doc_module_name":25,"category_name":121,"show_sort_weight":118,"slug":122},"World Cup","world-cup",{"id":55,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":55,"slug":125},"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":97,"slug":129},19,"General","general",{"code":4,"msg":82,"data":131},{"doc_id":79,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":112,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":55,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":80,"read_time":144,"preview_page_count":4},4398048949847,"https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267","Machine Learning For Enhanced Robotic-Assisted Stroke Assessment  \nby  \nFaranak Akbarifar  \nA thesis submitted to the  \nSchool of Computing in conformity with the requirements for the degree of Doctor of Philosophy  \nQueen’s University Kingston, Ontario, Canada September 2025  \nCopyright © Faranak Akbarifar, 2025  \nAbstract  \nStroke remains one of the leading causes of long-term adult disability worldwide, creating an urgent need for precise and objective neurological assessment methods to guide therapeutic interventions. Conventional assessment techniques primarily rely on clinician observations, which are inherently subjective and often lack the granularity required for personalized rehabilitation. To address these limitations, robotic platforms such as the Kinarm Exoskeleton Lab offer detailed, high-resolution kinematic data that promise more accurate, objective, and sensitive evaluations of motor impairment.  \nIn this thesis, we leverage advanced machine learning techniques to extract clinically relevant insights from Kinarm robotic data, addressing four major research challenges: (1) the limited exploration of machine learning approaches for interpreting robotic data,(2) shortcomings of existing one-class normative models which overlook subtle motor impairments, (3) challenges related to label ambiguity that impact generalization, and (4) the lengthy nature of full Kinarm assessments that could fatigue stroke survivors.  \nFirst, we successfully distinguished between stroke and control participants using Kinarm kinematics, employing unsupervised representation learning methods  \nthat identified and visualized subtle distinctions within clinical categories, revealing fine-grained patterns of impairment undetectable by standard assessment tools. Second, we introduce uncertainty-aware machine learning techniques to refine stroke and control classifications, significantly enhancing the model’s sensitivity to subtle neurological impairments by systematically handling ambiguous data points. Our findings indicate that filtering out the top 10% most uncertain data samples significantly increases sensitivity in distinguishing minimally impaired stroke survivors from healthy controls. Finally, we investigate foundation models for time series forecasting, enabling the prediction of additional robotic task trials based on limited initial data. This approach significantly reduces the required assessment duration without compromising diagnostic accuracy.  \nCollectively, this work demonstrates that integrating robotic data with innovative machine learning techniques not only enhances the sensitivity and objectivity of stroke impairment assessment but also paves the way toward personalized and efficient rehabilitation strategies.  \nCo-Authorship  \nThis thesis is primarily based on three publications resulting from collaborative research among multiple researchers. All publications have been modified to ensure thesis coherence.  \nChapter 3 builds on the following publication and contribution:  \n• Faranak Akbarifar, Sean P. Dukelow, Parvin Mousavi, Stephen H. Scott,“Computeraided identification of stroke-associated motor impairments using a virtual reality augmented robotic system”. In Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization, pages 252–259 . Taylor and Francis, 2022 .  \n• Contributions: Faranak Akbarifar conceived the concept and methodology, analyzed data, interpreted results, drafted the manuscript, and participated in editing. Sean P. Dukelow coordinated data collection, contributed to task design, and participated in editing. Stephen H. Scott coordinated data collection, contributed to task design, provided the concept and methodology, interpreted results, and participated in editing. Parvin Mousavi conceived the concept and methodology, interpreted results, and participated in editing. All authors  \nreviewed and approved the final manuscript before submission.  \nChapter 4 builds on the fol","cbCaiulYV3ojlr5r","https://ap.wps.com/l/cbCaiulYV3ojlr5r","pdf",5005826,162,"English","# Abstract\n## Co-Authorship\n### Chapter 3\n### Chapter 4\n### Chapter 5","[{\"question\":\"What problem does the thesis address in stroke assessment?\",\"answer\":\"It addresses the need for more precise and objective neurological assessment than clinician observations, which are subjective and may lack sufficient detail for personalized rehabilitation.\"},{\"question\":\"How does the thesis use robotic data in its machine learning approach?\",\"answer\":\"It leverages Kinarm Exoskeleton Lab kinematic data and applies machine learning to distinguish stroke from control participants and to extract fine-grained impairment patterns.\"},{\"question\":\"What strategies are proposed to improve classification sensitivity and reduce assessment time?\",\"answer\":\"The thesis introduces uncertainty-aware techniques to better handle ambiguous data and uses foundation models for time series forecasting to predict additional trials from limited initial data, reducing required assessment duration.\"}]","Machine Learning For Enhanced Robotic-Assisted Stroke Assessment - Doctor of Philosophy Thesis | PDF",408]