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This study tests the feasibility and signal behaviour of integrating surface electromyography (sEMG) with MDS-UPDRS-aligned tasks in healthy adults to derive normative low-frequency muscle activation profiles. Thirty-two participants performed seven upper-limb tasks while sEMG was recorded, normalised, filtered at 14 Hz, and analysed using FFT and STFT, revealing task-dependent differences mainly in 3.5–9 Hz.",{"@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":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/electromyography-emg-signal-processing-to-evaluate-low-frequency-tremors-abstract/463773/",{"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/electromyography-emg-signal-processing-to-evaluate-low-frequency-tremors-abstract/463773.png","ImageObject",300,407,{"name":92,"@type":93},"RuangKosong","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-06","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":29},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What is the main objective of the study?","Question",{"text":112,"@type":113},"To evaluate the feasibility and signal behaviour of combining surface EMG with MDS-UPDRS-aligned tasks in healthy participants, with the goal of establishing normative low-frequency muscle activation profiles.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How were the sEMG signals processed and analysed?",{"text":117,"@type":113},"Signals were normalised using maximum voluntary contraction, filtered at 14 Hz, and analysed using frequency-domain (FFT) and time-frequency (STFT) methods.",{"name":119,"@type":110,"acceptedAnswer":120},"Which frequency range showed the most task-dependent differences?",{"text":121,"@type":113},"Significant differences in both frequency occurrence and magnitude were observed mainly within the 3.5–9 Hz range.","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},463773,1791301632,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":29,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},962090883219,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","Article  \nElectromyography (EMG) Signal Processing to Evaluate Low-Frequency Tremors  \nSamantha O’Sullivan *, Mark Daly , Niall Murray  and Thiago Braga Rodrigues   \nAcademic Editor: Toshiyo Tamura  \nReceived: 30 November 2025  \nRevised: 22 December 2025  \nAccepted: 23 December 2025  \nPublished: 25 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.  \nDepartment of Engineering & Technology, The Technological University of the Shannon: Midlands Midwest,  \nN37 HD68 Athlone, Ireland; [mark.daly@tus.ie](mark.daly@tus.ie) (M.D.); [niall.murray@tus.ie](niall.murray@tus.ie) (N.M.); [thiago.braga@tus.ie](thiago.braga@tus.ie) (T.B.R.)  \n* Correspondence: [samantha.osullivan@tus.ie](samantha.osullivan@tus.ie)  \nAbstract  \nObjective quantification of tremor remains a challenge in Parkinson’s disease (PD) assessment, with current clinical assessments relying largely on subjective scale ratings. This study evaluates the feasibility and signal behaviour of integrating surface electromyography (sEMG) with MDS-UPDRS-aligned tasks in a healthy adult cohort, with the aim of establishing normative low-frequency muscle activation profiles. Thirty-two healthy participants (mean age 27.6 ± 5.3 years) completed seven upper-limb tasks derived from the MDS-UPDRS while sEMG data were recorded from antagonistic forearm muscles. Signals were normalised using maximum voluntary contraction, filtered at 14 Hz, and analysed using frequency-domain (FFT) and time-frequency (STFT) methods. Significant task-dependent differences were observed in both frequency occurrence and magnitude (p \u003C 0.05), particularly within the 3.5–9 Hz range. Finger tapping elicited increased lowfrequency activity compared to baseline, while pronation–supination produced the most stable and consistent muscle activation across participants. Frequencies above 12 Hz showed minimal task discrimination. These findings demonstrate that low-frequency tremor-like activity can occur during specific MDS-UPDRS tasks in healthy individuals and may require further validation before being considered suitable for PD staging. This work establishes normative sEMG benchmarks to support future clinical validation and PD cohort comparisons.  \nKeywords: movement disorders; tremors; electromyography; MDS-UPDRS; healthy subjects; task based; low frequency signals; signal processing  \n1. Introduction  \nParkinson’s disease (PD) is a progressive neurodegenerative disease characterised by a range of motor and non-motor symptoms, including bradykinesia, rigidity, postural instability, and tremor [1] . Tremor is often among the earliest and most recognisable motor manifestations and is typically described as an involuntary, rhythmic oscillation arising from alternating or synchronous muscle activation [2,3] . Despite its clinical relevance, tremor remains challenging to assess objectively, particularly in early or mild disease stages, where symptom presentation may be subtle and variable.  \nDiagnosis and management of PD is based on history and comprehensive assessments, using scales such as the MDS-UPDRS [4,5] . During these assessments, neurologists observe the patient during the performance of specific tasks and assigns scores based on standardised rating scales (e.g., 0–4 ratings) . These scales evaluate diverse areas of PD, analysing  \ndisease stage, quality of life, activities of daily living, impairment, disability, and many other aspects [6] .  \nAlthough computational tools have been introduced in clinical settings, their adoption remains inconsistent, and subjective scoring persists as the clinical gold standard. Inter-rate variability persists among different clinicians or medical centres conducting assessments [4,7] . With the development of sensing technologies, a variety of sensor-based devices have been used to assess PD. Among these, ","cbCaialqosXdsUTO","https://ap.wps.com/l/cbCaialqosXdsUTO","pdf",8056207,21,"English","# Introduction\n# Methodology\n## Study Design and Ethical Approval","[{\"question\":\"What is the main objective of the study?\",\"answer\":\"To evaluate the feasibility and signal behaviour of combining surface EMG with MDS-UPDRS-aligned tasks in healthy participants, with the goal of establishing normative low-frequency muscle activation profiles.\"},{\"question\":\"How were the sEMG signals processed and analysed?\",\"answer\":\"Signals were normalised using maximum voluntary contraction, filtered at 14 Hz, and analysed using frequency-domain (FFT) and time-frequency (STFT) methods.\"},{\"question\":\"Which frequency range showed the most task-dependent differences?\",\"answer\":\"Significant differences in both frequency occurrence and magnitude were observed mainly within the 3.5–9 Hz range.\"}]","Electromyography (EMG) Signal Processing to Evaluate Low-Frequency Tremors - Abstract | PDF",1790767124,53]