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It frames everyday task behavior, control reliability, and the mismatch between standardized clinical tests and real-life challenge scenarios. The work evaluates EMG feature-space metrics for prediction of algorithm performance and highlights their weakness for real-time human control. It argues that sensory limitations, intuitive yet learnable control mappings, and the anti-fragile adaptation to variation are central to dexterous prosthesis outcomes.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-myoelectric-prosthesis-control-towards-the-advancement-of-assessing-functional-use-and-control-skill-290571/290571/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-myoelectric-prosthesis-control-towards-the-advancement-of-assessing-functional-use-and-control-skill-290571/290571.png","ImageObject",300,407,{"name":92,"@type":93},"Liam","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-01","2026-09-17",true,{"@type":102,"interactionType":103,"userInteractionCount":34},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the thesis target in assessing myoelectric prosthesis performance?","Question",{"text":112,"@type":113},"It targets the gap between standardized clinical tests and real-life situations that challenge myoelectric prosthetic systems, emphasizing better assessment of functional use and control skill.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How does the thesis describe intuitive control?",{"text":117,"@type":113},"Intuitive control is defined as a clear and stable mapping between user input and motor output, where errors provide useful information for learning, rather than control without practice.",{"name":119,"@type":110,"acceptedAnswer":120},"Why are EMG feature-space metrics considered insufficient for real-time human control prediction?",{"text":121,"@type":113},"EMG feature space metrics such as separability index can predict offline algorithm performance, but they poorly predict real-time control performance of a human user.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},290571,1789642106,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":34,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":14,"language":139,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":140,"faqs":141,"seo_title":142,"seo_description":67,"update_tm":129,"read_time":24,"preview_page_count":4},8796095461564,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","University of Groningen  \nMachine-learning myoelectric prosthesis control: towards the advancement of assessing functional use and control skill  \nFranzke, Andreas  \nDOI:  \n10.33612/diss.663805052  \nIMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.  \nDocument Version  \nPublisher's PDF, also known as Version of record  \nPublication date: 2023  \nLink to publication in University of Groningen/UMCG research database  \nCitation for published version (APA):  \nFranzke, A. (2023) . Machine-learning myoelectric prosthesis control: towards the advancement of assessing functional use and control skill. [Thesis fully internal (DIV), University of Groningen] . University of Groningen. [https://doi.org/10.33612/diss.663805052](https://doi.org/10.33612/diss.663805052)  \nCopyright  \nOther than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) .  \nThe publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment.  \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.  \nDownloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.)  \nDownload date: 28-12-2025  \nPropositions  \n1. To accomplish (motor) tasks in daily life, prosthesis users will choose the path of least resistance – irrespective of whether that actively involves the prosthesis or not (this thesis) .  \n2. The “embodiment” of a prosthetic device also involves a deep and intuitive perception of the user about the chance of control failure and mechanical failure of the device (this thesis) .  \n3. Multi-function prosthetic hands are mostly used for stabilization and support during bimanual tasks (this thesis) .  \n4. The majority of the standardized clinical tests for hand prostheses avoid situations which challenge the (myoelectric) prosthetic systems whereas prosthesis users encounter such situations every day (this thesis) .  \n5. An increase in the (technological) complexity of hand prostheses might require an increase in the complexity of the test for such devices as well (this thesis) .  \n6. Intuitive control doesn’t mean control without practice, errors and learning – intuitive control means that there is a clear and stable mapping between user input and motor output, where every error provides the user with useful information about that mapping (this thesis) .  \n7. EMG feature space metrics such as the separability index have proven to predict the (offline) performance of machine-learning algorithms. However, so far, they poorly predict the real-time control performance of a human (this thesis) .  \n8. The real challenge in achieving dexterous motor control with a hand prosthesis isnot in the translation of muscle signals to prosthetic motion, but in the lack of sensory structures in individuals with a missing hand (Inspired by Gesslbauer et al., 2017) .  \n9. Healthy biological systems are not robust to errors, but they are “anti-fragile”, meaning they thrive under (a certain degree of ) variation and errors.  \n(Inspired by Taleb,","cbCaigZqV1JqfY8v","https://ap.wps.com/l/cbCaigZqV1JqfY8v","pdf",234050,"English","# Propositions\n## Daily-life task behavior and control failure perception\n## Use of multi-function hands and limitations of standardized tests\n## Test complexity, intuitive control mapping, and EMG metric predictability\n## Sensory limitations, anti-fragility, and control scheme evolution\n## Movement complexity and learning under practice","[{\"question\":\"What problem does the thesis target in assessing myoelectric prosthesis performance?\",\"answer\":\"It targets the gap between standardized clinical tests and real-life situations that challenge myoelectric prosthetic systems, emphasizing better assessment of functional use and control skill.\"},{\"question\":\"How does the thesis describe intuitive control?\",\"answer\":\"Intuitive control is defined as a clear and stable mapping between user input and motor output, where errors provide useful information for learning, rather than control without practice.\"},{\"question\":\"Why are EMG feature-space metrics considered insufficient for real-time human control prediction?\",\"answer\":\"EMG feature space metrics such as separability index can predict offline algorithm performance, but they poorly predict real-time control performance of a human user.\"}]","Machine-learning myoelectric prosthesis control: towards the advancement of assessing functional use and control skill | PDF"]