[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128412-en":3,"doc-seo-128412-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},128412,8796095027276,"Valentina","https://avatar.qwps.com/avatar/d3BzX2FwX3Rlc3RfMjUxMTI2XzAxODA=",8,"Research & Report","Signal Classification Based on a Hybrid Approach of Supervised and Unsupervised Machine Learning - Thesis Abstract","Optical sensors for automated prosthesis control must accurately infer which finger is moving using only forearm biosignals, enabling efficient and adaptive intelligent prosthetic systems. Reliable interpretation of these signals is essential for improving quality of life for people with motor disabilities. This thesis applies machine learning to classify finger movements from Fiber Bragg Grating sensor data collected from ten patients performing finger gestures.","Signal Classification Based on a Hybrid Approach of Supervised and Unsupervised Machine Learning  \nGregory M . Vergilino- a61451  \nThesis presented to the School of Technology and Management at the Polytechnic Institute of Bragança for the Master’s Degree in Eletrical and Computer Engineering within the scope of the Double Degree Program with the Federal Technological University of Paraná in Control and Automation Engineering.  \nWork oriented by:  \nProf. Ana I. Pereira  \nProf. Uilian J. Dreyer  \nProf. José Lima  \nBragança  \nSignal Classification Based on a Hybrid Approach of Supervised and Unsupervised Machine Learning  \nGregory M . Vergilino- a61451  \nThesis presented to the School of Technology and Management at the Polytechnic Institute of Bragança for the Master’s Degree in Eletrical and Computer Engineering within the scope of the Double Degree Program with the Federal Technological University of Paraná in Control and Automation Engineering.  \nWork oriented by:  \nProf. Ana I. Pereira  \nProf. Uilian J. Dreyer  \nProf. José Lima  \nBragança  \nDedication  \nGod blesses everyone. And I will always be grateful to Him for taking care of me in every step of my life. He is the Alpha, the Omega and Beyond. Amen!  \nTo my beloved family, those who taught me the true meaning of love. But specially to my brother , I want to dedicate in order to inspire him to pursue his dreams as I did. I believe in all your efforts and know you can be even better me.  \nAcknowledgement  \nFirst, I want to acknowledge how important God is in my life. Without Him, I would not have achieved everything I have so far. He means everything to me—my past, my future, and my faith.  \nTo the most important people in my life, my dad and mom, I want to express my deepest gratitude for showing me the path of honor and truth. I could not have asked fora better family, including my brother, for whom I have deep admiration.  \nTo my friends, I do not have enough words to express my appreciation. Lucas Scriptore, my true friend, who supported me throughout this journey, studying with me both in person and on Discord, and always helping me through the chaos. Felipe Bueno, thankyou very much for the time we spent together and for all the support, and I am thankful to Henrique as well. To my friends in Brazil, Luis, Maria and Samara, I express the same warmest and heartfelt thanks, always supporting my progress.  \nI would also like to thank both universities for the opportunity to complete this project, for all the financial support through the scholarship, and for the pedagogical guidance provided with excellence. Especially to Professor Ana Pereira, thank you for your patience, even when the unexpected happened. Your guidance was exceptional! Thank you so  \nmuch.  \nAbstract  \nThe main challenge in using optical sensors to collect data for automated prosthesis control lies in accurately predicting which finger is moving based solely on forearm signals. This is a complex task in the design of intelligent prosthetic systems, which must be both efficient and adaptive. However, improving the quality of life for individuals with motor disabilities demands reliable interpretation of such biosignals.  \nThis work proposes the use of machine learning algorithms to address this problem. In this research was used a dataset of signal acquired with a Fiber Bragg Grating sensor positioned on the forearm, on a group of ten patients. The group were asked realize some finger movements in order to gather data. The main problem is to identify which movement is being realized without labeling the signal. In this research will be analyzed methods to apply label on the data and classify them. The focus was to get a precise hybrid approach of supervised and unsupervised methods. k-Means was used as an unsupervised machine learning method to group similar data into distinct clusters and label the data. Random Forest was used as supervised learning algorithms to classify the data after labeling.  \nKe","cbCaie1vmlvPubKe","https://ap.wps.com/l/cbCaie1vmlvPubKe","pdf",3413796,3,1,92,"English","en",105,"# Abstract\n# Keywords","[{\"question\":\"What is the main challenge addressed in this research?\",\"answer\":\"Accurately predicting which finger is moving using only forearm signals collected by optical sensing.\"},{\"question\":\"How was the dataset collected?\",\"answer\":\"Signals were acquired with a Fiber Bragg Grating sensor on the forearm while ten patients performed finger movements to gather data.\"},{\"question\":\"Which hybrid machine learning methods are proposed?\",\"answer\":\"k-Means is used to cluster similar data without labels, then Random Forest classifies the movements after labeling derived from the clustering results.\"}]","Signal Classification Based on a Hybrid Approach of Supervised and Unsupervised Machine Learning - Thesis Abstract | PDF",1785947373,232,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"signal-classification-based-on-a-hybrid-approach-of-supervised-and-unsupervised-machine-learning-thesis-abstract","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/signal-classification-based-on-a-hybrid-approach-of-supervised-and-unsupervised-machine-learning-thesis-abstract/128412/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-26","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 is the main challenge addressed in this research?","Question",{"text":76,"@type":77},"Accurately predicting which finger is moving using only forearm signals collected by optical sensing.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How was the dataset collected?",{"text":81,"@type":77},"Signals were acquired with a Fiber Bragg Grating sensor on the forearm while ten patients performed finger movements to gather data.",{"name":83,"@type":74,"acceptedAnswer":84},"Which hybrid machine learning methods are proposed?",{"text":85,"@type":77},"k-Means is used to cluster similar data without labels, then Random Forest classifies the movements after labeling derived from the clustering results.","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":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]