Next Article in Journal / Special Issue
A Wearable System for the Evaluation of the Human-Horse Interaction: A Preliminary Study
Previous Article in Journal
A Raspberry Pi Cluster Instrumented for Fine-Grained Power Measurement
Previous Article in Special Issue
Robust and Accurate Algorithm for Wearable Stereoscopic Augmented Reality with Three Indistinguishable Markers
Article Menu

Export Article

Open AccessArticle
Electronics 2016, 5(4), 62; doi:10.3390/electronics5040062

Automatic Measurement of Chew Count and Chewing Rate during Food Intake

Department of Electrical Engineering, The University of Alabama, Tuscaloosa, AL 35487, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Enzo Pasquale Scilingo and Gaetano Valenza
Received: 19 July 2016 / Revised: 31 August 2016 / Accepted: 8 September 2016 / Published: 23 September 2016
View Full-Text   |   Download PDF [1355 KB, uploaded 23 September 2016]   |  

Abstract

Research suggests that there might be a relationship between chew count as well as chewing rate and energy intake. Chewing has been used in wearable sensor systems for the automatic detection of food intake, but little work has been reported on the automatic measurement of chew count or chewing rate. This work presents a method for the automatic quantification of chewing episodes captured by a piezoelectric sensor system. The proposed method was tested on 120 meals from 30 participants using two approaches. In a semi-automatic approach, histogram-based peak detection was used to count the number of chews in manually annotated chewing segments, resulting in a mean absolute error of 10.40 % ± 7.03%. In a fully automatic approach, automatic food intake recognition preceded the application of the chew counting algorithm. The sensor signal was divided into 5-s non-overlapping epochs. Leave-one-out cross-validation was used to train a artificial neural network (ANN) to classify epochs as “food intake” or “no intake” with an average F1 score of 91.09%. Chews were counted in epochs classified as food intake with a mean absolute error of 15.01% ± 11.06%. The proposed methods were compared with manual chew counts using an analysis of variance (ANOVA), which showed no statistically significant difference between the two methods. Results suggest that the proposed method can provide objective and automatic quantification of eating behavior in terms of chew counts and chewing rates. View Full-Text
Keywords: chewing rate; food intake detection; piezoelectric sensor; artificial neural network; feature computation; chew counting; peak detection chewing rate; food intake detection; piezoelectric sensor; artificial neural network; feature computation; chew counting; peak detection
Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Farooq, M.; Sazonov, E. Automatic Measurement of Chew Count and Chewing Rate during Food Intake. Electronics 2016, 5, 62.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top