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dc.contributor.authorThomaz, Edison
dc.contributor.authorEssa, Irfan
dc.contributor.authorAbowd, Gregory D.
dc.date.accessioned2016-04-19T19:23:51Z
dc.date.available2016-04-19T19:23:51Z
dc.date.issued2015
dc.identifier.citationThomaz, E., Essa, I., & Abowd, G. D. (2015). A Practical Approach for Recognizing Eating Moments with Wrist-Mounted Inertial Sensing. Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing (UbiComp '15), pp. 1029-1040.en_US
dc.identifier.isbn978-1-4503-3574-4
dc.identifier.urihttp://hdl.handle.net/1853/54746
dc.descriptionCopyright ©2015 ACMen_US
dc.descriptionDOI: 10.1145/2750858.2807545
dc.description.abstractRecognizing when eating activities take place is one of the key challenges in automated food intake monitoring. Despite progress over the years, most proposed approaches have been largely impractical for everyday usage, requiring multiple on-body sensors or specialized devices such as neck collars for swallow detection. In this paper, we describe the implementation and evaluation of an approach for inferring eating moments based on 3-axis accelerometry collected with a popular off-the-shelf smartwatch. Trained with data collected in a semi-controlled laboratory setting with 20 subjects, our system recognized eating moments in two free-living condition studies (7 participants, 1 day; 1 participant, 31 days), with F-scores of 76.1% (66.7% Precision, 88.8% Recall), and 71.3% (65.2% Precision, 78.6% Recall). This work represents a contribution towards the implementation of a practical, automated system for everyday food intake monitoring, with applicability in areas ranging from health research and food journaling.en_US
dc.language.isoen_USen_US
dc.publisherGeorgia Institute of Technologyen_US
dc.subjectActivity recognitionen_US
dc.subjectAutomated dietary assessmenen_US
dc.subjectDietary intakeen_US
dc.subjectFood journalingen_US
dc.subjectInertial sensorsen_US
dc.titleA Practical Approach for Recognizing Eating Moments With Wrist-Mounted Inertial Sensingen_US
dc.typePre-printen_US
dc.typeProceedingsen_US
dc.contributor.corporatenameGeorgia Institute of Technology. Institute for Robotics and Intelligent Machinesen_US
dc.contributor.corporatenameGeorgia Institute of Technology. College of Computingen_US
dc.contributor.corporatenameGeorgia Institute of Technology. School of Interactive Computingen_US
dc.publisher.originalAssociation for Computing Machinery
dc.identifier.doi10.1145/2750858.2807545
dc.embargo.termsnullen_US


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