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Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor

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dc.contributor.authorYang, Xin
dc.contributor.authorDoulah, Abul
dc.contributor.authorFarooq, Muhammad
dc.contributor.authorParton, Jason
dc.contributor.authorMcCrory, Megan A.
dc.contributor.authorHiggins, Janine A.
dc.contributor.authorSazonov, Edward
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.contributor.otherBoston University
dc.contributor.otherUniversity of Colorado Anschutz Medical Campus
dc.contributor.otherUniversity of Colorado Denver
dc.date.accessioned2023-09-28T19:33:28Z
dc.date.available2023-09-28T19:33:28Z
dc.date.issued2019
dc.description.abstractAccurate and objective assessment of energy intake remains an ongoing problem. We used features derived from annotated video observation and a chewing sensor to predict mass and energy intake during a meal without participant self-report. 30 participants each consumed 4 different meals in a laboratory setting and wore a chewing sensor while being videotaped. Subject-independent models were derived from bite, chew, and swallow features obtained from either video observation or information extracted from the chewing sensor. With multiple regression analysis, a forward selection procedure was used to choose the best model. The best estimates of meal mass and energy intake had (mean +/- standard deviation) absolute percentage errors of 25.2% +/- 18.9% and 30.1% +/- 33.8%, respectively, and mean +/- standard deviation estimation errors of -17.7 +/- 226.9 g and -6.1 +/- 273.8 kcal using features derived from both video observations and sensor data. Both video annotation and sensor-derived features may be utilized to objectively quantify energy intake.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationYang, X., Doulah, A., Farooq, M., Parton, J., McCrory, M. A., Higgins, J. A., & Sazonov, E. (2019). Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor. In Scientific Reports (Vol. 9, Issue 1). Springer Science and Business Media LLC. https://doi.org/10.1038/s41598-018-37161-x
dc.identifier.doi10.1038/s41598-018-37161-x
dc.identifier.orcidhttps://orcid.org/0000-0002-8161-6602
dc.identifier.orcidhttps://orcid.org/0000-0002-4273-194X
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11419
dc.languageEnglish
dc.language.isoen_US
dc.publisherNature Portfolio
dc.rights.licenseAttribution 4.0 International (CC BY 4.0)
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectPERSONAL DIGITAL ASSISTANT
dc.subjectFOOD-INTAKE
dc.subjectDIETARY ASSESSMENT
dc.subjectINGESTIVE BEHAVIOR
dc.subjectEATING HABITS
dc.subjectCHILDREN
dc.subjectVALIDATION
dc.subjectACCURACY
dc.subjectMONITOR
dc.subjectMultidisciplinary Sciences
dc.titleStatistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensoren_US
dc.typeArticle
dc.typetext

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