{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/127243"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/127243","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Advance restless leg syndrome monitoring with deep learning","abstract":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_html":"Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","abstract_has_math":false,"creators":["Yu, Hang"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Computer Science","degree_department":null,"school":null,"contributors":["Wang, Yuxiong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2024,"date_issued":"2024-12-12","date_published":"2024-12-12","updated_at":"2026-07-22T22:25:03Z","subjects":["Restless Leg Syndrome","Deep Learning","Transfer Learning"],"languages":["en","eng"],"rights":["Copyright 2024 Hang Yu"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/127243","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Wang, Yuxiong"]},{"key":"dc:creator","label":"Author","values":["Yu, Hang"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2024-12-12","2024-12"]},{"key":"dc:type","label":"Dc Type","values":["text","Thesis"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computer Science"]},{"key":"thesis:degree_level","label":"Degree Level","values":["Thesis"]},{"key":"thesis:degree_name","label":"Degree Name","values":["M.S."]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["University of Illinois at Urbana-Champaign"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Restless Leg Syndrome","Deep Learning","Transfer Learning"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en","eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2024 Hang Yu"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/127243"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Hang Yu, accepted the attached license on 2024-12-12 at 13:43.","The student, Hang Yu, submitted this Thesis for approval on 2024-12-12 at 13:48.","This Thesis was approved for publication on 2024-12-12 at 14:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21439 on 2025-03-28 at 14:27:40","Periodic Limb Movements (PLMs) are frequently observed in patients with Restless Legs Syndrome (RLS). While electromyography (EMG) of leg muscles is traditionally used to quantify the motor symptom burden of RLS, wearable trackers may cause discomfort to patients. This study investigates the efficacy of pressure-sensing mat data in detecting PLMs, offering a non-invasive alternative for monitoring limb movements during sleep. Our approach utilizes a pressure-sensing mat that captures subtle changes in pressure distribution, providing a comfortable method for continuous monitoring. We collected a comprehensive dataset comprising 153.5 hours of synchronized pressure mat and EMG recordings from 21 patients. Ground truth labels were derived from concurrent EMG data, ensuring reliable annotations for PLM detection. We also propose to finetune a deep learning model, 3D-ResNet18 with pretrained Kinetics700k weights, as a baseline for this dataset that predicts PLMs from pressure mat data. Our approach offers a comfortable alternative to EMG-based detection, opening new avenues for continuous home-based monitoring."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Advance restless leg syndrome monitoring with deep learning"]}]}],"canonical_facts":{"dc:contributor":["Wang, Yuxiong"],"dc:creator":["Yu, Hang"],"dc:date":["2024-12-12","2024-12"],"dc:description":["Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2025-03-28 without embargo terms","The student, Hang Yu, accepted the attached license on 2024-12-12 at 13:43.","The student, Hang Yu, submitted this Thesis for approval on 2024-12-12 at 13:48.","This Thesis was approved for publication on 2024-12-12 at 14:55.","DSpace SAF Submission Ingestion Package generated from Vireo submission #21439 on 2025-03-28 at 14:27:40","Periodic Limb Movements (PLMs) are frequently observed in patients with Restless Legs Syndrome (RLS). While electromyography (EMG) of leg muscles is traditionally used to quantify the motor symptom burden of RLS, wearable trackers may cause discomfort to patients. This study investigates the efficacy of pressure-sensing mat data in detecting PLMs, offering a non-invasive alternative for monitoring limb movements during sleep. Our approach utilizes a pressure-sensing mat that captures subtle changes in pressure distribution, providing a comfortable method for continuous monitoring. We collected a comprehensive dataset comprising 153.5 hours of synchronized pressure mat and EMG recordings from 21 patients. Ground truth labels were derived from concurrent EMG data, ensuring reliable annotations for PLM detection. We also propose to finetune a deep learning model, 3D-ResNet18 with pretrained Kinetics700k weights, as a baseline for this dataset that predicts PLMs from pressure mat data. Our approach offers a comfortable alternative to EMG-based detection, opening new avenues for continuous home-based monitoring."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/127243"],"dc:language":["en","eng"],"dc:rights":["Copyright 2024 Hang Yu"],"dc:subject":["Restless Leg Syndrome","Deep Learning","Transfer Learning"],"dc:title":["Advance restless leg syndrome monitoring with deep learning"],"dc:type":["text","Thesis"],"thesis:degree_discipline":["Computer Science"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:25:03Z"}