Asset Details
Do you wish to reserve the book?
Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor
Hey, we have placed the reservation for you!
By the way, why not check out events that you can attend while you pick your title.
Oops! Something went wrong.
Looks like we were not able to place the reservation. Kindly try again later.
Are you sure you want to remove the book from the shelf?
Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor
Do you wish to request the book?
Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor
Please be aware that the book you have requested cannot be checked out. If you would like to checkout this book, you can reserve another copy
We have requested the book for you!
Your request is successful and it will be processed during the Library working hours. Please check the status of your request in My Requests.
Oops! Something went wrong.
Looks like we were not able to place your request. Kindly try again later.
Journal Article
Statistical models for meal-level estimation of mass and energy intake using features derived from video observation and a chewing sensor
2019
Overview
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.
Publisher
Nature Publishing Group UK,Nature Publishing Group
Subject
/ Adult
/ Chewing
/ Female
/ Humanities and Social Sciences
/ Humans
/ Male
/ Multiple regression analysis
/ Science
/ Sensors
