[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124685-en":3,"doc-seo-124685-105":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},124685,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine Learning in Tremor Analysis - Critique and Directions","Tremor is the most frequent human movement disorder, and diagnosis relies on clinical assessment, which is often difficult to make accurately. Fine-tuning clinical criteria and device-based qualitative analysis have improved diagnostic accuracy, while accelerometric recordings enable high-resolution characterization. Machine-learning models may support less-biased classification and quantification, but improper implementation can create non-generalizable findings and flawed conclusions. This work reviews recent supervised ML developments for tremor research, outlines opportunities, limitations, and future directions, and emphasizes clinical translation and responsible use.","R E V I E W  \nMachine Learning in Tremor Analysis: Critique and Directions  \nAnwesan De, 1,2 Kailash P. Bhatia, MD, FRCP,3  Jens Volkmann, MD, PhD, FEAN, 1 Robert Peach, PhD, 1,4 and  \nSebastian R. Schreglmann, MD, PhD, FEBN 1*   \n1Department of Neurology, University Hospital Wuerzburg, Wuerzburg, Germany 2Department of Electronics and Instrumentation, Birla Institute of Technology and Science, Pilani, Hyderabad, India 3Department of Clinical and Movement Neurosciences, Institute of Neurology, UCL, London, United Kingdom 4Department of Brain Sciences, Imperial College London, London, United Kingdom  \n\n| \u003Cbr>ABSTRACT: Tremor is the most frequent human movement disorder, and its diagnosis is based on clinical assessment. Yet finding the accurate clinical diagnosis isnot always straightforward. Fine-tuning of clinical diagnostic criteria over the past few decades, as well as devicebased qualitative analysis, has resulted in incremental improvements to diagnostic accuracy. Accelerometric assessments are commonplace, enabling clinicians to capture high-resolution oscillatory properties of tremor, which recently have been the focus of various machine-learning (ML) studies. In this context, the application of ML models to accelerometric recordings provides the potential for less-biased classification and quantification of tremor disorders. However, if implemented incorrectly, ML can result in spurious or nongeneralizable results and misguided conclusions. This work summarizes and highlights recent developments in ML tools for tremor research, with a focus\u003Cbr> | \u003Cbr>on supervised ML. We aim to highlight the opportunities and limitations of such approaches and provide future directions while simultaneously guiding the reader through the process of applying ML to analyze tremor data. We identify the need for the movement disorder community to take a more proactive role in the application of these novel analytical technologies, which so far have been predominantly pursued by the engineering and data analysis field. Ultimately, big-data approaches offer the possibility to identify generalizable patterns but warrant meaningful translation into clinical practice. © 2023 The Authors. Movement Disorders published by Wiley Periodicals LLCon behalf of International Parkinson and Movement Disorder Society.\u003Cbr>Key Words: accelerometer; artificial intelligence; classification; feature based\u003Cbr> |\n| --- | --- |\n\nThis is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.  \n*Correspondence to: Dr. Sebastian R. Schreglmann, Department of Neurology, University Hospital Wuerzburg, Josef-Schneiderstr. 11, 97080 Wuerzburg, Germany; E-mail: sebastian.schreglmann@gmai[l.com](l.com); [schreglman_s@ukw.de](schreglman_s@ukw.de)  \nRelevant conflicts of interest/financial disclosure: Authors report no conflict of interest.  \nFunding agency: This work has not received dedicated financial support.  \nRobert Peach and Sebastian R. Schreglmann contributed equally.  \nReceived: 26 September 2022; Revised: 16 January 2023; Accepted:  \n17 February 2023  \nPublished online in Wiley Online Library  \n([wileyonlinelibrary.com](wileyonlinelibrary.com). DOI: 10.1002/mds.29376)[). DOI: 10.1002/mds.29376](wileyonlinelibrary.com). DOI: 10.1002/mds.29376)  \nIntroduction  \nTremor is the most common disturbance of movement in man, defined as an involuntary rhythmic, oscillating movement of a body part. Oscillatory movements are a function of a mechanical component, that is, the inherent mechanical propensity of an object to oscillate, and a central component, that is, the result of an activation of agonist–antagonist muscles by rhythmic central nervous system activity.1 Clinically, tremor presents with a particular body distribution (affecting limbs, the head, neck, jaw, voca","cbCaihk9LELXPthB","https://ap.wps.com/l/cbCaihk9LELXPthB","pdf",840986,1,16,"English","en",105,"# Introduction\n## Tremor disorders as clinical diagnoses\n## Need for objective tremor measures\n## Accelerometry and feature extraction\n## Limits of simple tremor metrics\n## Validated methods for differentiating ET and Parkinson’s disease","[{\"question\":\"Why is tremor diagnosis challenging in clinical practice?\",\"answer\":\"Accurate diagnosis is not always straightforward because tremor disorders are primarily clinical diagnoses and can be difficult to phenotype precisely. Reported misdiagnosis rates reach up to 37% or even 50%.\"},{\"question\":\"What role do accelerometers play in machine learning for tremor analysis?\",\"answer\":\"Accelerometric assessments are widely used to capture high-resolution oscillatory properties of tremor. These recordings provide quantifiable features that can be fed into machine-learning algorithms.\"},{\"question\":\"What are the main risks when applying machine learning to tremor data?\",\"answer\":\"If ML is implemented incorrectly, it can yield spurious results or outcomes that do not generalize, leading to misguided conclusions. The article emphasizes careful, responsible application and translation into clinical practice.\"}]","Machine Learning in Tremor Analysis - Critique and Directions | PDF",1785893909,40,{"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},"machine-learning-in-tremor-analysis-critique-and-directions","",{"@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/machine-learning-in-tremor-analysis-critique-and-directions/124685/",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","2026-08-05",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 tremor diagnosis challenging in clinical practice?","Question",{"text":75,"@type":76},"Accurate diagnosis is not always straightforward because tremor disorders are primarily clinical diagnoses and can be difficult to phenotype precisely. Reported misdiagnosis rates reach up to 37% or even 50%.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What role do accelerometers play in machine learning for tremor analysis?",{"text":80,"@type":76},"Accelerometric assessments are widely used to capture high-resolution oscillatory properties of tremor. These recordings provide quantifiable features that can be fed into machine-learning algorithms.",{"name":82,"@type":73,"acceptedAnswer":83},"What are the main risks when applying machine learning to tremor data?",{"text":84,"@type":76},"If ML is implemented incorrectly, it can yield spurious results or outcomes that do not generalize, leading to misguided conclusions. 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