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Machine Learning Methods for Activity Detection in Wearable Sensor Data Streams

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dc.contributor.advisorBenjamin Marlin
dc.contributor.authorAdams, Roy
dc.contributor.departmentUniversity of Massachusetts Amherst
dc.date2023-09-23 21:40:04
dc.date.accessioned2024-04-26T15:25:55Z
dc.date.available2024-04-26T15:25:55Z
dc.date.submittedSeptember
dc.date.submitted2018
dc.description.abstractWearable wireless sensors have the potential for transformative impact on the fields of health and behavioral science. Recent advances in wearable sensor technology have made it possible to simultaneously collect multiple streams of physiological and context data from individuals in natural environments; however, extracting reliable high-level inferences from these raw data streams remains a key data analysis challenge. In this dissertation, we address three challenges that arise when trying to perform activity detection from wearable sensor streams. First, we address the challenge of learning from small amounts of noisy data by proposing a class of conditional random field models for activity detection. We apply this model class to three different activity detection problems, improving performance in all three when compared with standard independent and structured models. Second, we address the challenge of inferring activities from long input sequences by evaluating strategies for pruning the inference dynamic programs used in structured prediction models. We apply these strategies to the proposed structured activity detection models resulting in inference speedups ranging from 66x to 257x with little to no decrease in predictive performance. Finally, we address the challenge of learning from imprecise annotations by proposing a weak supervision framework for learning discrete-time detection models from imprecise continuous-time observations. We apply this framework to both independent and structured models and demonstrate improved performance over weak supervision baselines.
dc.description.degreeDoctor of Philosophy (PhD)
dc.description.departmentComputer Science
dc.identifier.doihttps://doi.org/10.7275/0nsf-5682
dc.identifier.orcidN/A
dc.identifier.urihttps://hdl.handle.net/20.500.14394/17523
dc.relation.urlhttps://scholarworks.umass.edu/cgi/viewcontent.cgi?article=2417&context=dissertations_2&unstamped=1
dc.source.statuspublished
dc.subjectmobile health
dc.subjectmachine learning
dc.subjectweakly supervised learning
dc.subjectstructured prediction
dc.subjectwearable sensors
dc.subjectArtificial Intelligence and Robotics
dc.titleMachine Learning Methods for Activity Detection in Wearable Sensor Data Streams
dc.typeopenaccess
dc.typedissertation
digcom.contributor.authorisAuthorOfPublication|email:rjadams@cs.umass.edu|institution:University of Massachusetts Amherst|Adams, Roy
digcom.identifierdissertations_2/1318
digcom.identifier.contextkey12547986
digcom.identifier.submissionpathdissertations_2/1318
dspace.entity.typePublication
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