[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118677-en":3,"doc-seo-118677-105":30,"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":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},118677,13056703020460,"Valentina","https://ap-avatar.wpscdn.com/avatar/be000253dac470eee5d?_k=1778207105932848923",7,"Healthcare","Essential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning","Essential tremor (ET) is a frequent neurological disorder that demands precise diagnosis and severity stratification to support personalized, effective management. The study presents a machine learning pipeline for ET severity evaluation using the gold-standard Archimedes spiral test, leveraging a family-based dataset spanning mild to advanced stages. The approach combines PCA, LDA, and SVM classifiers and incorporates Fahn–Tolosa–Marin Tremor Rating Scale (FMT-TRS) to distinguish tremor presence and severity, validated via cross-validation and Gaussian noise perturbation tests. Findings support a non-invasive, clinically relevant diagnostic framework for practice and telemedicine.","Article  \nEssential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning  \nJose Ignacio Sánchez Méndez 1,2,*, Elsa Fernandez 2,3, Alberto Bergareche 4 and Karmele Lopez-de-Ipina 2,5,6, *  \nAcademic Editor: Tao Liu  \nReceived: 25 November 2025  \nRevised: 19 December 2025  \nAccepted: 26 December 2025  \nPublished: 31 December 2025  \nCopyright: © 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.  \n1 NTT DATA EU & LATAM USA Branch Inc., 4100 North Fairfax Drive, Suite 810, Arlington, TX 22203, USA  \n2 EleKin Research Group, University of the Basque Country (EHU), 20018 Donostia, Spain  \n3 Department of Computational Science and Artificial Intelligence, University of the Basque Country (EHU), 20018 Donostia, Spain; [elsa.fernandez@ehu.eus](elsa.fernandez@ehu.eus)  \n4 Movement Disorders Unit, Department of Neurology, University Hospital Donostia, Paseo Doctor Begiristain, 109, 20014 Donostia, Spain; [abergare@proton.me](abergare@proton.me)  \n5 Department of Systems Engineering and Automation, University of the Basque Country (EHU),  \n20018 Donostia, Spain  \n6 Department of Psychiatry, University of Cambridge, Herchel Smith Building Forvie Site Robinson Way Cambridge, Cambridge CB2 0SZ, UK  \n* Correspondence: jisanchez003@ikasle.ehu.eus or [spolex@msn.com](spolex@msn.com) (J.I.S.M.); [karmele.ipina@ehu.eus or kl505@cam.ac.uk](karmele.ipina@ehu.eus or kl505@cam.ac.uk) (K.L.-d.-I.)  \nAbstract  \nBackground: Essential tremor (ET) is among the most common neurological disorders, requiring precise diagnosis and severity assessment for personalized and effective management. Methods: This study explores an innovative approach to evaluate ET severity using the gold-standard Archimedes spiral test. The family-based dataset covers the entire range of tremor severity, from very mild (level 1) to advanced stages, offering a valuable resource for studying early diagnosis and tracking disease progression. The proposed method introduces a machine learning pipeline that combines Principal Component Analysis (PCA), linear discriminant analysis (LDA), and support vector machines (SVMs) to classify ET severity based on Archimedean spiral radius data. Results: By incorporating the Fahn–Tolosa–Marin Tremor Rating Scale (FMT-TRS), the pipeline effectively distinguishes between tremor presence and severity. Its robustness was demonstrated through rigorous cross-validation and tests involving Gaussian noise perturbations. Conclusions: These results underscore the machine learning-based pipeline’s potential as a non-invasive and trustworthy diagnostic tool for clinical use and telemedicine applications. Moreover, the combination of geometric features, FMT-TRS scores, clinically oriented evaluation metrics, and classical statistical and machine learning models offers a robust, interpretable, explainable, and clinically meaningful analytical framework.  \nKeywords: classification algorithms; essential tremor; personalized medicine; handwriting analysis; linear discriminant analysis; machine learning; principal component analysis; support vector machines  \n1. Introduction  \nEssential tremor (ET) affects millions of individuals globally, often leading to significant motor disability and reduced quality of life. Recent prevalence studies suggest that ET is one of the most common movement disorders, with increasing recognition of its heterogeneity and impact [1] . The Movement Disorder Society defines essential tremor (ET) as an isolated tremor syndrome characterized by a bilateral upper-limb action tremor with  \na minimum duration of three years [2,3], which may or may not be accompanied by tremor in other regions, such as the head, voice [4], or lower limbs, and with no other neurological impairments, as seen in Parkinson’s disease [5] . The disease can become highly disabling and, in familial cases [6], ","cbCaikrVJtVl4EeW","https://ap.wps.com/l/cbCaikrVJtVl4EeW","pdf",4247828,1,26,"English","en",105,"# Introduction\n## Definition, prevalence, and clinical challenge of ET\n## Handwriting analysis and the Archimedes spiral test\n# Methods\n## Machine learning pipeline (PCA, LDA, SVM)\n## Feature extraction from spiral data\n## Incorporating FMT-TRS and evaluation strategy\n# Results\n## Classification of tremor presence and severity\n## Cross-validation and robustness to noise\n# Conclusions","[{\"question\":\"How does the study assess essential tremor severity?\",\"answer\":\"It uses handwriting analysis based on the Archimedes spiral test and a machine learning pipeline to classify tremor presence and severity from spiral radius data.\"},{\"question\":\"Which machine learning methods are included in the proposed pipeline?\",\"answer\":\"The pipeline combines Principal Component Analysis (PCA), linear discriminant analysis (LDA), and support vector machines (SVMs).\"},{\"question\":\"How was the robustness of the method evaluated?\",\"answer\":\"The approach was validated with rigorous cross-validation and tests that include Gaussian noise perturbations to simulate input disturbances.\"}]","Essential Tremor Severity Assessment Using Handwriting Analysis and Machine Learning | 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does the study assess essential tremor severity?","Question",{"text":76,"@type":77},"It uses handwriting analysis based on the Archimedes spiral test and a machine learning pipeline to classify tremor presence and severity from spiral radius data.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which machine learning methods are included in the proposed pipeline?",{"text":81,"@type":77},"The pipeline combines Principal Component Analysis (PCA), linear discriminant analysis (LDA), and support vector machines (SVMs).",{"name":83,"@type":74,"acceptedAnswer":84},"How was the robustness of the method evaluated?",{"text":85,"@type":77},"The approach was validated with rigorous cross-validation and tests that include Gaussian noise perturbations to simulate input 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