[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127223-en":3,"doc-seo-127223-105":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},127223,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Machine Learning-based Method to Label Signals from People with Neurological Injuries","Interfaces using sEMG must identify true muscle activation while separating it from noise and interference, yet many existing studies rely on data from healthy individuals. This work analyzes sEMG from people with neurological injuries, including cerebral palsy and post-stroke, using machine learning to classify muscle contractions and rest without pre-processing. Signals were acquired from triceps brachii and extensor carpi radialis, segmented into time windows, and evaluated with SVM, Random Forest, and an ensemble voting classifier.","Machine Learning-based Method to Label Signals from People  \nwith Neurological Injuries  \nJoão Pedro Moreto Lourenção  \nPostgraduate Program in Electrical Engineering and Industrial Informatics (CPGEI)– UTFPR-CT [jlourencao@alunos.utfpr.edu.br](jlourencao@alunos.utfpr.edu.br)  \nAndré Eugênio Lazzaretti  \nPostgraduate Program in Electrical Engineering and Industrial Informatics (CPGEI)– UTFPR-CT [andrelazzaretti@gmail.com](andrelazzaretti@gmail.com)  \nCristian Veggian Matias  \nPostgraduate Program in Electrical Engineering and Industrial Informatics (CPGEI)– UTFPR-CT [cristianveggian@gmail.com](cristianveggian@gmail.com)  \nMaría Verónica González Méndez  \nPostgraduate Program in Electrical Engineering and Industrial Informatics (CPGEI)– UTFPR-CT [mariaveronica@alunos.utfpr.edu.br](mariaveronica@alunos.utfpr.edu.br)  \nJosé Jair Alves Mendes Júnior  \nPostgraduate Program in Electrical Engineering and Industrial Informatics (CPGEI)– UTFPR-CT [jjjunior@utfpr.edu.br](jjjunior@utfpr.edu.br)  \nABSTRACT  \nInterfaces that use sEMG signals face the challenge of correctly identifying the signal while distinguishing it from noise or interference. Although classical techniques like visual inspection and machine learning methods exist, most studies focus on signals from healthy individuals. There is a lack of data and methods suitable for signals from individuals with neurological conditions, such as cerebral palsy and post-stroke. This study analyzes sEMG data from individuals with neurological injuries, using machine learning methods to identify muscle contractions and rest without pre-processing. The data were acquired from people with neurological diseases, such as cerebral palsy and post-stroke. They were extracted using sEMG from triceps brachii and extensor carpi radialis muscles. The signals were not preprocessed and were input as segmented time windows to three proposed classifiers: Support Vector Machine, Random Forest and an Ensemble Voting classifier. All three classifiers reached around 99% accuracy and F1-Score on typical sEMG data, but the results on abnormal data were inconclusive.  \nKEYWORDS  \nOnset Detection, Machine Learning, sEMG, Support Vector Machine, Random Forest  \n1 INTRODUCTION  \nTechnological advancements and globalization have facilitated the development of Assistive Technology, which includes tools and strategies to enhance the autonomy and quality of life for individuals with disabilities [1] . Globally, 15% of the population has some form of disability [2], and in Brazil, 8.9% of individuals are affected, with 1.4% experiencing difficulties in manual tasks such as handling objects [3] . A 2021 study by The Lancet Neurology, in collaboration with the World Health Organization, reported that over three billion people worldwide live with neurological conditions, including stroke, neonatal encephalopathy, migraine, dementia, diabetic neuropathy, meningitis, epilepsy, autism spectrum disorder, and nervous system cancers [4] .  \nThe myoelectric signal (electromyography, EMG) is a synthetic behavior of compound action potentials (APs) generated by a series of motor units (MUs) being electrically or neurologically activated [5]. While myoelectric signals can be detected invasively using needle electrodes, this method could present some discomfort to the user during the acquisition [6]. Instead, an alternative way called surface electromyography (sEMG) allows the capture of the signals in surface skin, acquiring the signals generated by motor unit action potentials propagating through skeleton muscles [7] . It provides insights into the specific movements those muscles execute [8] .  \nTaking as a reference people with neurological diseases, several assistive technologies are developed to aid in their needs [9, 10] . In some of them, sEMG signals are used to control activities, such as an interface that allows commands to be sent to everyday electronics such as a television, a radio, lamps [11], and even electrical stim","cbCaijlL1auJl3wv","https://ap.wps.com/l/cbCaijlL1auJl3wv","pdf",953721,1,7,"English","en",105,"# Abstract\n# Introduction\n## Assistive technology and neurological conditions\n## EMG and sEMG signal acquisition\n## sEMG signal identification challenge\n## Related work","[{\"question\":\"What problem does this study address in sEMG-based interfaces?\",\"answer\":\"Correctly identifying sEMG activation while distinguishing it from noise or interference. Many prior works focus on healthy subjects, leaving a gap for neurological conditions.\"},{\"question\":\"How was the sEMG data collected and prepared for classification?\",\"answer\":\"sEMG was extracted from triceps brachii and extensor carpi radialis muscles, segmented into time windows, and fed into classifiers without pre-processing.\"},{\"question\":\"Which classifiers were tested, and how did they perform?\",\"answer\":\"Support Vector Machine, Random Forest, and an Ensemble Voting classifier were tested. They achieved around 99% accuracy and F1-score on typical sEMG data, while results on abnormal data were inconclusive.\"}]","Machine Learning-based Method to Label Signals from People with Neurological Injuries | PDF",1785937613,18,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-based-method-to-label-signals-from-people-with-neurological-injuries","",{"@graph":36,"@context":86},[37,54,69],{"@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-based-method-to-label-signals-from-people-with-neurological-injuries/127223/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-21","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},"What problem does this study address in sEMG-based interfaces?","Question",{"text":76,"@type":77},"Correctly identifying sEMG activation while distinguishing it from noise or interference. Many prior works focus on healthy subjects, leaving a gap for neurological conditions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the sEMG data collected and prepared for classification?",{"text":81,"@type":77},"sEMG was extracted from triceps brachii and extensor carpi radialis muscles, segmented into time windows, and fed into classifiers without pre-processing.",{"name":83,"@type":74,"acceptedAnswer":84},"Which classifiers were tested, and how did they perform?",{"text":85,"@type":77},"Support Vector Machine, Random Forest, and an Ensemble Voting classifier were tested. They achieved around 99% accuracy and F1-score on typical sEMG data, while results on abnormal data were inconclusive.","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":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]