Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor
McCrory, Megan A.
Higgins, Janine A.
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Citation (published version)Xin Yang, Abul Doulah, Muhammad Farooq, Jason Parton, Megan A McCrory, Janine A Higgins, Edward Sazonov. 2019. "Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor." SCIENTIFIC REPORTS, Volume 9. https://doi.org/10.1038/s41598-018-37161-x
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.
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