{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/121283"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/121283","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Video-based Parkinson's disease detection in low data regimes","abstract":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-08-01","abstract_html":"Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-08-01","abstract_has_math":false,"creators":["Sriram, Pranav"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Electrical & Computer Engr","degree_department":null,"school":null,"contributors":["Wang, Yuxiong"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2023,"date_issued":"2023-08","date_published":"2023-08","updated_at":"2026-07-22T22:24:57Z","subjects":["Computer Vision","Deep Learning"],"languages":["en","eng"],"rights":["Copyright 2023 Pranav Sriram"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/2142/121283","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":["Sriram, Pranav"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2023-08","2023-07-21"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Electrical & Computer Engr"]},{"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":["Computer Vision","Deep 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 2023 Pranav Sriram"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://hdl.handle.net/2142/121283"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-08-01","The student, Pranav Sriram, accepted the attached license on 2023-07-20 at 18:30.","The student, Pranav Sriram, submitted this Thesis for approval on 2023-07-20 at 18:34.","This Thesis was approved for publication on 2023-07-21 at 09:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19764 on 2023-12-04 at 17:36:09","Parkinson’s disease is a prevalent neurodegenerative disorder affecting mil- lions of people worldwide. Its characteristic symptoms manifest themselves in an increasingly severe fashion as the disease progresses. Thus, early diag- nosis is vital in slowing the disease progression and initiating the appropriate treatment in a timely manner. Moreover, Parkinson’s disease detection is a nontrivial task, encompassing many multimodal cues such as speech, appear- ance and muscle movement for accurate diagnosis. The difficulty of the task opens up the avenue of AI-enhanced disease detection through intelligent, state-of-the-art models leveraging all the aforementioned cues. Existing works have explored a variety of different techniques for Parkin- son’s disease diagnosis, ranging from monitoring breathing signals to tracking muscular movement for tremor and hypomimia detection. However, these works all use their own proprietary medical datasets, which are not released due to patient confidentiality. As a result, the performance of models across different works cannot be compared, hindering the progress towards strong performance on this task. Moreover, existing techniques also do not leverage state-of-the-art vision or deep learning methodologies and rather use simpler deep learning models with reduced learning capabilities. For our methodology development, we construct our novel dataset PD- Dataset composed of clips extracted from videos. These clips contain both healthy controls (celebrities without Parkinson’s) and Parkinson’s patients, providing a comprehensive benchmark for model evaluation. In addition, we propose a new detection model based on state-of-the-art video understanding architectures and demonstrate strong performance on our novel benchmark."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Video-based Parkinson's disease detection in low data regimes"]}]}],"canonical_facts":{"dc:contributor":["Wang, Yuxiong"],"dc:creator":["Sriram, Pranav"],"dc:date":["2023-08","2023-07-21"],"dc:description":["Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-08-01","The student, Pranav Sriram, accepted the attached license on 2023-07-20 at 18:30.","The student, Pranav Sriram, submitted this Thesis for approval on 2023-07-20 at 18:34.","This Thesis was approved for publication on 2023-07-21 at 09:28.","DSpace SAF Submission Ingestion Package generated from Vireo submission #19764 on 2023-12-04 at 17:36:09","Parkinson’s disease is a prevalent neurodegenerative disorder affecting mil- lions of people worldwide. Its characteristic symptoms manifest themselves in an increasingly severe fashion as the disease progresses. Thus, early diag- nosis is vital in slowing the disease progression and initiating the appropriate treatment in a timely manner. Moreover, Parkinson’s disease detection is a nontrivial task, encompassing many multimodal cues such as speech, appear- ance and muscle movement for accurate diagnosis. The difficulty of the task opens up the avenue of AI-enhanced disease detection through intelligent, state-of-the-art models leveraging all the aforementioned cues. Existing works have explored a variety of different techniques for Parkin- son’s disease diagnosis, ranging from monitoring breathing signals to tracking muscular movement for tremor and hypomimia detection. However, these works all use their own proprietary medical datasets, which are not released due to patient confidentiality. As a result, the performance of models across different works cannot be compared, hindering the progress towards strong performance on this task. Moreover, existing techniques also do not leverage state-of-the-art vision or deep learning methodologies and rather use simpler deep learning models with reduced learning capabilities. For our methodology development, we construct our novel dataset PD- Dataset composed of clips extracted from videos. These clips contain both healthy controls (celebrities without Parkinson’s) and Parkinson’s patients, providing a comprehensive benchmark for model evaluation. In addition, we propose a new detection model based on state-of-the-art video understanding architectures and demonstrate strong performance on our novel benchmark."],"dc:format":["application/pdf"],"dc:identifier":["https://hdl.handle.net/2142/121283"],"dc:language":["en","eng"],"dc:rights":["Copyright 2023 Pranav Sriram"],"dc:subject":["Computer Vision","Deep Learning"],"dc:title":["Video-based Parkinson's disease detection in low data regimes"],"dc:type":["text"],"thesis:degree_discipline":["Electrical & Computer Engr"],"thesis:degree_level":["Thesis"],"thesis:degree_name":["M.S."],"thesis:institution_name":["University of Illinois at Urbana-Champaign"]},"updated_at":"2026-07-22T22:24:57Z"}