[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128431-en":3,"doc-seo-128431-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128431,962084925290,"Ophelia","https://ap-avatar.wpscdn.com/davatar_085a072bc5b1113ac321206ff7593b45",8,"Research & Report","Leveraging Machine Learning Tools to Develop Objective, Interpretable, and Accessible Assessments of Postural Instability in Parkinson’s Disease - Dissertation","Parkinson’s disease (PD) is a leading neurodegenerative disorder, and postural instability (PI) is among its most disabling motor symptoms due to frequent falls and loss of independence. PI shows limited response to existing PD treatments, and current clinical evaluations remain subjective, introducing human error. Improved diagnostic tools are needed to quantify PI objectively, assess additional postural tasks, and support more frequent measurement than semiannual clinical visits. This dissertation develops two machine-learning approaches: markerless pose estimation for reactive step length during perturbations and insole plantar-pressure modeling for daily balance tasks. Results show accurate PD classification, differentiation of fallers versus non-fallers, and clinically useful, interpretable quantitative measures.","Leveraging Machine Learning Tools to Develop Objective, Interpretable, and Accessible Assessments of Postural Instability in Parkinson’s Disease  \nA DISSERTATION  \nSUBMITTED TO THE FACULTY OF THE GRADUATE SCHOOL OF THE UNIVERSITY OF MINNESOTA  \nBY  \nCara Herbers  \nIN PARTIAL FULFILLMENT OF THE REQUIREMENTS  \nFOR THE DEGREE OF  \nDOCTOR OF PHILOSOPHY  \nDr. Arthur Erdman and Dr. Matthew Johnson  \nApril, 2023  \n➞ Cara Herbers 2023  \nALL RIGHTS RESERVED  \nAcknowledgements  \nThank you to University of Minnesota’s Department of Mechanical Engineering and National Science Foundation, and to The Mike Justak Foundation for Parkinson’s Disease for providing funds to make this work possible.  \nDedication  \nTo husband and my parents. Thank you for your endless encourgaement and support. I could not have completed this without you.  \nAbstract  \nParkinson’s disease (PD) is the second most common neurodegenerative disease in the United States, affecting 1 million Americans. PD-related postural instability (PI) is oneof the most disabling motor symptoms of PD since it is associated with increased falls and loss of independence. PI has little or no response to current PD treatments, the underlying mechanisms are poorly understood, and the current clinical assessments are subjective and introduce human error.  \nThere is a need for improved diagnostic tools of PI for clinicians to better characterize, understand, and treat PD-related PI. Several criteria are necessary to address this clinical need: (1) the clinical rating of PI should be quantified objectively,(2) additional postural tasks should be clinically assessed and quantified, and (3) the assessments of PI should occur more frequently than a biannual clinical assessment. This project sought to develop two novel approaches to address these criteria.  \nFirst, deep learning markerless pose estimation was leveraged to assess reactive step length in response to shoulder pull and surface translation perturbations for individuals with and without PD. Reactive step length was altered in PD (significantly for treadmill perturbations, and with an insignificant trend for shoulder pull perturbations), and improved by dopamine replacement therapy. Next, insole plantar pressure sensor data from 111 subjects (44 PD, 67 controls) were collected and used to assess PD-related PI during typical daily balance tasks. Machine learning models were developed to accurately identify PD from young controls (area under the curve (AUC) 0.99 +/- 0.00), PD from age-matched controls (AUC 0.99 +/-0.01), and PD non-fallers from PDfallers (AUC 0.91 +/- 0.08) . It was seen that utilizing features from both static and active tasks significantly improved classification performances and that all tasks were useful for separating controls from PD; however, tasks with higher postural threat were preferred for separating PD non-fallers from PD fallers.  \nThis work produced numerous clinical and translational implications. Notably, (1) simple and accessible quantitative measures can be used to identify PD and individuals with PD who fall, and (2) machine learning models can be leveraged for implementing, quantifying, and interpreting these measures into something clinically useful.  \nContents  \nAcknowledgements i  \nDedication ii  \nAbstract iii  \nContents iv  \nList of Tables ix  \nList of Figures xi  \n1 Introduction 1  \n1.1 Parkinson’s Disease .............................. 1  \n1.1.1 Overview ............................... 1  \n1.1.2 Clinical Evaluation .......................... 2  \n1.1.3 Telehealth Interventions ....................... 5  \n1.1.4 Disease Management ......................... 6  \n1.2 Postural Control and Instability ....................... 7  \n1.2.1 Postural Control Overview ..................... 7  \n1.2.2 Postural Instability in Parkinson’s ................. 10  \n1.3 Characterizing Postural Instability in Parkinson’s ............. 14  \n1.3.1 Clinical Characterization ...................... 14  \n1.3.2 Quantitative Cha","cbCaiko3OfaIYwFP","https://ap.wps.com/l/cbCaiko3OfaIYwFP","pdf",27695615,2,1,231,"English","en",105,"# Acknowledgements\n# Dedication\n# Abstract\n# Contents\n# List of Tables\n# List of Figures\n# Introduction\n## Parkinson’s Disease\n## Postural Control and Instability\n## Characterizing Postural Instability in Parkinson’s\n## Introduction to Machine Learning\n## Motivation and Research Aims\n# Altered Reactive Step Length in Parkinson’s can be Captured with Deep Learning Motion Tracking\n## Introduction\n## Study Protocol\n## Step Length Extraction Methods\n## Step Length Extraction Results\n## Step Length Extraction Conclusion","[{\"question\":\"Why is improving postural instability assessment in Parkinson’s disease important?\",\"answer\":\"Postural instability is linked to increased falls and loss of independence, and current assessments are subjective. Limited treatment responsiveness and poor understanding of mechanisms make more reliable diagnostic tools necessary.\"},{\"question\":\"How did the dissertation quantify reactive step length in Parkinson’s-related postural instability?\",\"answer\":\"It used deep learning markerless pose estimation to measure reactive step length during shoulder-pull and surface-translation perturbations. Reactive step length differed in PD and improved with dopamine replacement therapy.\"},{\"question\":\"How were plantar pressure data used to identify Parkinson’s and distinguish fallers from non-fallers?\",\"answer\":\"Insole plantar pressure sensor data from 111 subjects were modeled with machine learning to classify PD versus controls and PD non-fallers versus fallers. Features from both static and active tasks improved performance, with higher-threat tasks helping separate non-fallers from fallers.\"}]","Leveraging Machine Learning Tools to Develop Objective, Interpretable, and Accessible Assessments of Postural Instability in Parkinson’s Disease - Dissertation | PDF",1785947659,582,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"leveraging-machine-learning-tools-to-develop-objective-interpretable-and-accessible-assessments-of-postural-instability-in-parkinsons-disease-dissertation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/leveraging-machine-learning-tools-to-develop-objective-interpretable-and-accessible-assessments-of-postural-instability-in-parkinsons-disease-dissertation/128431/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-24","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is improving postural instability assessment in Parkinson’s disease important?","Question",{"text":76,"@type":77},"Postural instability is linked to increased falls and loss of independence, and current assessments are subjective. Limited treatment responsiveness and poor understanding of mechanisms make more reliable diagnostic tools necessary.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How did the dissertation quantify reactive step length in Parkinson’s-related postural instability?",{"text":81,"@type":77},"It used deep learning markerless pose estimation to measure reactive step length during shoulder-pull and surface-translation perturbations. Reactive step length differed in PD and improved with dopamine replacement therapy.",{"name":83,"@type":74,"acceptedAnswer":84},"How were plantar pressure data used to identify Parkinson’s and distinguish fallers from non-fallers?",{"text":85,"@type":77},"Insole plantar pressure sensor data from 111 subjects were modeled with machine learning to classify PD versus controls and PD non-fallers versus fallers. Features from both static and active tasks improved performance, with higher-threat tasks helping separate non-fallers from fallers.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]