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dc.contributor.advisorGhovanloo, Maysam
dc.contributor.authorOlubanjo, Temiloluwa O.
dc.date.accessioned2016-08-22T12:25:15Z
dc.date.available2016-08-22T12:25:15Z
dc.date.created2016-08
dc.date.issued2016-08-02
dc.date.submittedAugust 2016
dc.identifier.urihttp://hdl.handle.net/1853/55683
dc.description.abstractAutomatic food intake monitoring using wearable sensor-based systems is an alternative to manual self-report methods. Automatic methods aim to quantitatively track aspects related to eating, drinking and/or any form of energy consumption in an effort to encourage healthier dietary behaviors.In this dissertation, a detailed evaluation of research work in the field was undertaken to outline pros and cons of various sensing modalities for on-body use. The most relevant signal processing and machine learning techniques were identified, including best features for acoustic-, image-, and motion- based methods. To address some of the observed research gaps, we focused more on acoustic-based sensing of food intake activities and developed the first real-time swallowing detection algorithm. Following this, we introduced a tracheal activity recognition algorithm based on sub-optimally sampled acoustic signals for energy efficiency purposes. Another observed research gap relates to detecting dietary activities in noisy environments particularly for acoustic-based monitoring systems that are highly affected by background noise. To this effect, we developed a source separation method using semi-supervised non-negative matrix factorization for the enhancement of food intake acoustics in noisy recordings. We also introduced a low-cost template-matching method to detect food intake acoustics in very low signal-to-noise ratio recordings. This research work contributes to the development of a robust, sensor-based, wearable dietary monitoring system. Such a system aims to curtail the growing crisis of obesity, diabetes, eating disorders and other related chronic conditions.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectBody sensor network
dc.subjectDietary monitoring
dc.subjectMachine learning
dc.subjectObesity
dc.subjectSignal processing
dc.subjectWearable systems
dc.titleTowards automatic food intake monitoring using wearable sensor-based systems
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentElectrical and Computer Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberMoore II, Elliot
dc.contributor.committeeMemberInan, Omer
dc.contributor.committeeMemberAbowd, Gregory D.
dc.contributor.committeeMemberStarner, Thad
dc.date.updated2016-08-22T12:25:15Z


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