TY - GEN
T1 - Measuring children's eating behavior with a wearable device
AU - Bi, Shengjie
AU - Lu, Yiyang
AU - Tobias, Nicole
AU - Ryan, Ella
AU - Masterson, Travis
AU - Sen, Sougata
AU - Halter, Ryan
AU - Sorber, Jacob
AU - Gilbert-Diamond, Diane
AU - Kotz, David
N1 - Funding Information:
This research results from a research program at the Center for Technology and Behavioral Health (CTBH) at Dartmouth College, supported by the National Science Foundation under award numbers CNS-1565269, CNS-1835983, CNS-1565268, and CNS-1835974, and the Eunice Kennedy Shriver National Institute of Child Health and Human Development under award number R01HD092604. The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the sponsors.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/11
Y1 - 2020/11
N2 - Poor eating habits in children and teenagers can lead to obesity, eating disorders, or life-Threatening health problems. Although researchers have studied children's eating behavior for decades, the research community has had limited technology to support the observation and measurement of fine-grained details of a child's eating behavior. In this paper, we present the feasibility of adapting the Auracle, an existing research-grade earpiece designed to automatically and unobtrusively recognize eating behavior in adults, for measuring children's eating behavior. We identified and addressed several challenges pertaining to monitoring eating behavior in children, paying particular attention to device fit and comfort. We also improved the accuracy and robustness of the eating-Activity detection algorithms. We used this improved prototype in a lab study with a sample of 10 children for 60 total sessions and collected 22.3 hours of data in both meal and snack scenarios. Overall, we achieved an accuracy exceeding 85.0% and an F1 score exceeding 84.2% for eating detection with a 3-second resolution, and a 95.5% accuracy and a 95.7% F1 score for eating detection with a 1-minute resolution.
AB - Poor eating habits in children and teenagers can lead to obesity, eating disorders, or life-Threatening health problems. Although researchers have studied children's eating behavior for decades, the research community has had limited technology to support the observation and measurement of fine-grained details of a child's eating behavior. In this paper, we present the feasibility of adapting the Auracle, an existing research-grade earpiece designed to automatically and unobtrusively recognize eating behavior in adults, for measuring children's eating behavior. We identified and addressed several challenges pertaining to monitoring eating behavior in children, paying particular attention to device fit and comfort. We also improved the accuracy and robustness of the eating-Activity detection algorithms. We used this improved prototype in a lab study with a sample of 10 children for 60 total sessions and collected 22.3 hours of data in both meal and snack scenarios. Overall, we achieved an accuracy exceeding 85.0% and an F1 score exceeding 84.2% for eating detection with a 3-second resolution, and a 95.5% accuracy and a 95.7% F1 score for eating detection with a 1-minute resolution.
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U2 - 10.1109/ICHI48887.2020.9374304
DO - 10.1109/ICHI48887.2020.9374304
M3 - Conference contribution
AN - SCOPUS:85103211582
T3 - 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
BT - 2020 IEEE International Conference on Healthcare Informatics, ICHI 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Healthcare Informatics, ICHI 2020
Y2 - 30 November 2020 through 3 December 2020
ER -
