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

Accurate 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 laborato...

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Bibliographic Details
Published in:Scientific reports 2019-01, Vol.9 (1), p.45-45, Article 45
Main Authors: Yang, Xin, Doulah, Abul, Farooq, Muhammad, Parton, Jason, McCrory, Megan A., Higgins, Janine A., Sazonov, Edward
Format: Article
Language:English
Subjects:
639/166/985
639/166/987
Adult
Chewing
Energy Intake
Feeding Behavior
Female
Humanities and Social Sciences
Humans
Male
Mastication
Mathematical models
Middle Aged
Models, Statistical
multidisciplinary
Multiple regression analysis
Science
Science (multidisciplinary)
Sensors
Standard deviation
Statistical analysis
Statistical models
Young Adult
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Summary:Accurate 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.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-018-37161-x