Document Detail

A Sensor System for Automatic Detection of Food Intake Through Non-Invasive Monitoring of Chewing.
MedLine Citation:
PMID:  22675270     Owner:  NLM     Status:  Publisher    
Objective and automatic sensor systems to monitor ingestive behavior of individuals arise as a potential solution to replace inaccurate method of self-report. This paper presents a simple sensor system and related signal processing and pattern recognition methodologies to detect periods of food intake based on non-invasive monitoring of chewing. A piezoelectric strain gauge sensor was used to capture movement of the lower jaw from 20 volunteers during periods of quiet sitting, talking and food consumption. These signals were segmented into non-overlapping epochs of fixed length and processed to extract a set of 250 time and frequency domain features for each epoch. A forward feature selection procedure was implemented to choose the most relevant features, identifying from 4 to 11 features most critical for food intake detection. Support vector machine classifiers were trained to create food intake detection models. Twenty-fold cross-validation demonstrated per-epoch classification accuracy of 80.98% and a fine time resolution of 30 s. The simplicity of the chewing strain sensor may result in a less intrusive and simpler way to detect food intake. The proposed methodology could lead to the development of a wearable sensor system to assess eating behaviors of individuals.
Edward S Sazonov; Juan M Fontana
Publication Detail:
Journal Detail:
Title:  IEEE sensors journal     Volume:  12     ISSN:  1530-437X     ISO Abbreviation:  -     Publication Date:  2012  
Date Detail:
Created Date:  2012-6-7     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  101212357     Medline TA:  IEEE Sens J     Country:  -    
Other Details:
Languages:  ENG     Pagination:  1340-1348     Citation Subset:  -    
Department of Electrical and Computer Engineering, University of Alabama, Tuscaloosa, AL 35487-0286 USA.
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