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dc.contributor.authorFarooq, Muhammaden_US
dc.contributor.authorDoulah, Abulen_US
dc.contributor.authorParton, Jasonen_US
dc.contributor.authorMcCrory, Megan A.en_US
dc.contributor.authorHiggins, Janine A.en_US
dc.contributor.authorSazonov, Edwarden_US
dc.coverage.spatialSwitzerlanden_US
dc.date2019-03-07
dc.date.accessioned2020-05-15T14:54:53Z
dc.date.available2020-05-15T14:54:53Z
dc.date.issued2019-03-13
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/30871173
dc.identifier.citationMuhammad Farooq, Abul Doulah, Jason Parton, Megan A. McCrory, Janine A. Higgins, Edward Sazonov. 2019. "Validation of Sensor-Based Food Intake Detection by Multicamera Video Observation in an Unconstrained Environment.." Nutrients, Volume 11, Issue 3, https://doi.org/10.3390/nu11030609
dc.identifier.issn2072-6643
dc.identifier.urihttps://hdl.handle.net/2144/40902
dc.description.abstractVideo observations have been widely used for providing ground truth for wearable systems for monitoring food intake in controlled laboratory conditions; however, video observation requires participants be confined to a defined space. The purpose of this analysis was to test an alternative approach for establishing activity types and food intake bouts in a relatively unconstrained environment. The accuracy of a wearable system for assessing food intake was compared with that from video observation, and inter-rater reliability of annotation was also evaluated. Forty participants were enrolled. Multiple participants were simultaneously monitored in a 4-bedroom apartment using six cameras for three days each. Participants could leave the apartment overnight and for short periods of time during the day, during which time monitoring did not take place. A wearable system (Automatic Ingestion Monitor, AIM) was used to detect and monitor participants' food intake at a resolution of 30 s using a neural network classifier. Two different food intake detection models were tested, one trained on the data from an earlier study and the other on current study data using leave-one-out cross validation. Three trained human raters annotated the videos for major activities of daily living including eating, drinking, resting, walking, and talking. They further annotated individual bites and chewing bouts for each food intake bout. Results for inter-rater reliability showed that, for activity annotation, the raters achieved an average (±standard deviation (STD)) kappa value of 0.74 (±0.02) and for food intake annotation the average kappa (Light's kappa) of 0.82 (±0.04). Validity results showed that AIM food intake detection matched human video-annotated food intake with a kappa of 0.77 (±0.10) and 0.78 (±0.12) for activity annotation and for food intake bout annotation, respectively. Results of one-way ANOVA suggest that there are no statistically significant differences among the average eating duration estimated from raters' annotations and AIM predictions (p-value = 0.19). These results suggest that the AIM provides accuracy comparable to video observation and may be used to reliably detect food intake in multi-day observational studies.en_US
dc.description.sponsorshipP30 DK048520 - NIDDK NIH HHS; R01DK100796 - National Institute of Diabetes and Digestive and Kidney Diseasesen_US
dc.languageEnglish
dc.language.isoen_US
dc.relation.ispartofNutrients
dc.rights© 2019 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.en_US
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectAIMen_US
dc.subjectChewing detectionen_US
dc.subjectDietary assessmenten_US
dc.subjectFood intake detectionen_US
dc.subjectNeural networksen_US
dc.subjectObesityen_US
dc.subjectSensor validationen_US
dc.subjectVideo annotationen_US
dc.subjectActivities of daily livingen_US
dc.subjectAdulten_US
dc.subjectEatingen_US
dc.subjectFemaleen_US
dc.subjectHumansen_US
dc.subjectMaleen_US
dc.subjectMasticationen_US
dc.subjectMonitoring, physiologicen_US
dc.subjectReproducibility of resultsen_US
dc.subjectVideo recordingen_US
dc.titleValidation of sensor-based food intake detection by multicamera video observation in an unconstrained environment.en_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.3390/nu11030609
pubs.elements-sourcepubmeden_US
pubs.notesEmbargo: No embargoen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Health & Rehabilitation Sciences: Sargent Collegeen_US
pubs.organisational-groupBoston University, College of Health & Rehabilitation Sciences: Sargent College, Health Sciencesen_US
pubs.publication-statusPublished onlineen_US
dc.identifier.mycv454722


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© 2019 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
Except where otherwise noted, this item's license is described as © 2019 by the authors. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.