NDLI: Detection of Food Intake from Swallowing Sequences by Supervised and Unsupervised Methods

Content Provider	Springer Nature Link
Author	Lopez Meyer, Paulo Makeyev, Oleksandr Schuckers, Stephanie Melanson, Edward L. Neuman, Michael R. Sazov, Edward
Copyright Year	2010
Abstract	Studies of food intake and ingestive behavior in free-living conditions most often rely on self-reporting-based methods that can be highly inaccurate. Methods of Monitoring of Ingestive Behavior (MIB) rely on objective measures derived from chewing and swallowing sequences and thus can be used for unbiased study of food intake with free-living conditions. Our previous study demonstrated accurate detection of food intake in simple models relying on observation of both chewing and swallowing. This article investigates methods that achieve comparable accuracy of food intake detection using only the time series of swallows and thus eliminating the need for the chewing sensor. The classification is performed for each individual swallow rather than for previously used time slices and thus will lead to higher accuracy in mass prediction models relying on counts of swallows. Performance of a group model based on a supervised method (SVM) is compared to performance of individual models based on an unsupervised method (K-means) with results indicating better performance of the unsupervised, self-adapting method. Overall, the results demonstrate that highly accurate detection of intake of foods with substantially different physical properties is possible by an unsupervised system that relies on the information provided by the swallowing alone.
Starting Page	2766
Ending Page	2774
Page Count	9
File Format	PDF
ISSN	00906964
Journal	Annals of Biomedical Engineering
Volume Number	38
Issue Number	8
e-ISSN	15739686
Language	English
Publisher	Springer US
Publisher Date	2010-03-30
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Monitoring of ingestive behavior Food intake detection Obesity Eating disorders Wearable devices Support Vector Machines K-means clustering Biochemistry Mechanics Biophysics and Biological Physics Biomedical Engineering Biomedicine general
Content Type	Text
Resource Type	Article
Subject	Biomedical Engineering

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