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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Date
2019-01-10
Authors
Yang, Xin
Doulah, Abul
Farooq, Muhammad
Parton, Jason
McCrory, Megan A.
Higgins, Janine A.
Sazonov, Edward
Version
Published version
OA Version
Citation
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
Abstract
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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Copyright © The Author(s) 2019. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.