{"id":{"repo_id":"uiuc","oai_identifier":"oai:www.ideals.illinois.edu:2142/101099"},"canonical_url":"https://search.dev.ndltd.org/etd/uiuc/oai:www.ideals.illinois.edu:2142/101099","repository":{"repo_id":"uiuc","name":"University of Illinois - Urbana-Champaign","base_url":"https://www.ideals.illinois.edu/oai-pmh"},"display":{"title":"Dimensionality reduction in the control of quasi-static force production tasks in humans","abstract":"Human muscle activation patterns have proven difficult to characterize due to the large number of degrees of freedom present in the system. As a result, efforts to effectively reduce the number of degrees of freedom used to characterize this system have become an important area of research. The underlying characteristic behind the reduction in dimensionality is the ability to group together individual degrees of freedom (typically muscles) together to create new variables that act as the input to the system. In these experiments, two common grouping methods are explored: principal component analysis (PCA) and non-negative matrix factorization (NMF) subjected to a generalized Akaike information criterion (AIC) to serve as a quality of fit estimator. These are used to group the muscle activity of individuals during quasi-static force production tasks to synthesize reduced-order models that account for 90\\% of the muscle activity contributing to the task. Regression techniques are then utilized to obtain mathematical models that describe the system's behavior. Ultimately, we show that the system's response is a multi-valued function and linear combinations of predetermined functions perform poorly in terms of goodness of the fit at capturing the data. However, a Fourier series parameterization through time yields promising results in terms of validity of studying reduced order models and how they may be used to further study robotic systems or form movement characterization and rehabilitation infrastructure.","abstract_html":"Human muscle activation patterns have proven difficult to characterize due to the large number of degrees of freedom present in the system. As a result, efforts to effectively reduce the number of degrees of freedom used to characterize this system have become an important area of research. The underlying characteristic behind the reduction in dimensionality is the ability to group together individual degrees of freedom (typically muscles) together to create new variables that act as the input to the system. In these experiments, two common grouping methods are explored: principal component analysis (PCA) and non-negative matrix factorization (NMF) subjected to a generalized Akaike information criterion (AIC) to serve as a quality of fit estimator. These are used to group the muscle activity of individuals during quasi-static force production tasks to synthesize reduced-order models that account for 90\\% of the muscle activity contributing to the task. Regression techniques are then utilized to obtain mathematical models that describe the system's behavior. Ultimately, we show that the system's response is a multi-valued function and linear combinations of predetermined functions perform poorly in terms of goodness of the fit at capturing the data. However, a Fourier series parameterization through time yields promising results in terms of validity of studying reduced order models and how they may be used to further study robotic systems or form movement characterization and rehabilitation infrastructure.","abstract_has_math":false,"creators":["Speidel, Nathaniel Christopher"],"institution":"University of Illinois at Urbana-Champaign","degree_name":"M.S.","degree_level":"Thesis","degree_discipline":"Mechanical Engineering","degree_department":null,"school":null,"contributors":["LaValle, Steven M.","López-Ortiz, Citlali","Dullerud, Geir E."],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2018,"date_issued":"2018-09-04T20:32:04Z","date_published":"2018-09-04T20:32:04Z","updated_at":"2026-07-22T22:24:38Z","subjects":["Motor Control","Model Reduction","Principal Component Analysis","Non-negative Matrix Factorization"],"languages":["en"],"rights":["Copyright 2018, Nathaniel Speidel"],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/2142/101099","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["LaValle, Steven M.","López-Ortiz, Citlali","Dullerud, Geir E."]},{"key":"dc:creator","label":"Author","values":["Speidel, Nathaniel Christopher"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2018-09-04T20:32:04Z","2018-04-27","2018-05"]},{"key":"dc:type","label":"Dc Type","values":["text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Mechanical Engineering"]},{"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":["Motor Control","Model Reduction","Principal Component Analysis","Non-negative Matrix Factorization"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Copyright 2018, Nathaniel Speidel"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/2142/101099"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Human muscle activation patterns have proven difficult to characterize due to the large number of degrees of freedom present in the system. As a result, efforts to effectively reduce the number of degrees of freedom used to characterize this system have become an important area of research. The underlying characteristic behind the reduction in dimensionality is the ability to group together individual degrees of freedom (typically muscles) together to create new variables that act as the input to the system. In these experiments, two common grouping methods are explored: principal component analysis (PCA) and non-negative matrix factorization (NMF) subjected to a generalized Akaike information criterion (AIC) to serve as a quality of fit estimator. These are used to group the muscle activity of individuals during quasi-static force production tasks to synthesize reduced-order models that account for 90\\% of the muscle activity contributing to the task. Regression techniques are then utilized to obtain mathematical models that describe the system's behavior. Ultimately, we show that the system's response is a multi-valued function and linear combinations of predetermined functions perform poorly in terms of goodness of the fit at capturing the data. However, a Fourier series parameterization through time yields promising results in terms of validity of studying reduced order models and how they may be used to further study robotic systems or form movement characterization and rehabilitation infrastructure.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Nathaniel Speidel, accepted the attached license on 2018-04-27 at 10:58.","The student, Nathaniel Speidel, submitted this Thesis for approval on 2018-04-27 at 11:05.","This Thesis was approved for publication on 2018-04-27 at 14:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12536 on 2018-08-31 at 17:15:15","Made available in DSpace on 2018-09-04T20:32:04Z (GMT). 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The underlying characteristic behind the reduction in dimensionality is the ability to group together individual degrees of freedom (typically muscles) together to create new variables that act as the input to the system. In these experiments, two common grouping methods are explored: principal component analysis (PCA) and non-negative matrix factorization (NMF) subjected to a generalized Akaike information criterion (AIC) to serve as a quality of fit estimator. These are used to group the muscle activity of individuals during quasi-static force production tasks to synthesize reduced-order models that account for 90\\% of the muscle activity contributing to the task. Regression techniques are then utilized to obtain mathematical models that describe the system's behavior. Ultimately, we show that the system's response is a multi-valued function and linear combinations of predetermined functions perform poorly in terms of goodness of the fit at capturing the data. However, a Fourier series parameterization through time yields promising results in terms of validity of studying reduced order models and how they may be used to further study robotic systems or form movement characterization and rehabilitation infrastructure.","Submission original under an indefinite embargo labeled 'Open Access'. The submission was exported from vireo on 2018-08-31 without embargo terms","The student, Nathaniel Speidel, accepted the attached license on 2018-04-27 at 10:58.","The student, Nathaniel Speidel, submitted this Thesis for approval on 2018-04-27 at 11:05.","This Thesis was approved for publication on 2018-04-27 at 14:57.","DSpace SAF Submission Ingestion Package generated from Vireo submission #12536 on 2018-08-31 at 17:15:15","Made available in DSpace on 2018-09-04T20:32:04Z (GMT). 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