Physical Quantification of the Interactions Between Environment, Physiology, and Human Performance

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dc.contributor.advisorLary, David J.
dc.contributor.advisorAkbar, Mohammad
dc.contributor.committeeMemberHeelis, Roderick A.
dc.contributor.committeeMemberIzen, Joseph M.
dc.contributor.committeeMemberGolden, Richard M.
dc.contributor.committeeMemberAnderson, Phillip C.
dc.creatorTalebi, Shawhin
dc.date.accessioned2024-08-30T19:11:51Z
dc.date.available2024-08-30T19:11:51Z
dc.date.created2022-05
dc.date.issued2022-05
dc.date.submittedMay 2022
dc.date.updated2024-08-30T19:11:52Z
dc.description.abstractCharacterizing key physical interactions between the human body and an environmental context has countless important applications in public health, preventative healthcare, city planning, sports medicine, aviation, and more. However, the complexity of these multi- faceted interactions makes physical first principles approaches challenging. This manuscript presents a data-driven experimental paradigm that brings together holistic physical sensing and a range of computational tools to generate empirical machine learning models that quantify the interactions between environment, human physiology, and performance. The two key products of this paradigm are, 1) high fidelity predictive models, and 2) objective evaluation of predictor variable impacts on target variables. For example, in one case study, particulate concentrations were accurately inferred from the biometric observations alone using an empirical machine learning model. Next, evaluation model predictors revealed body temperature as the most important predictor of particulate concentrations. This flexible paradigm is used in multiple contexts to provide practical insights into the high-dimensional, interconnected dynamics of environment, physiology and human performance.
dc.format.mimetypeapplication/pdf
dc.identifier.uri
dc.identifier.urihttps://hdl.handle.net/10735.1/10115
dc.language.isoen
dc.subjectPhysics, General
dc.subjectHealth Sciences, General
dc.subjectArtificial Intelligence
dc.titlePhysical Quantification of the Interactions Between Environment, Physiology, and Human Performance
dc.typeThesis
dc.type.materialtext
local.embargo.lift2024-05-01
local.embargo.terms2024-05-01
thesis.degree.collegeSchool of Natural Sciences and Mathematics
thesis.degree.departmentPhysics
thesis.degree.grantorThe University of Texas at Dallas
thesis.degree.namePHD

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