More Web Proxy on the site http://driver.im/

research-article

Empirical Mode Decomposition of Throat Microphone Recordings for Intake Classification

Authors:

M.A. Tuğtekin Turan,

Engin ErzinAuthors Info & Claims

MMHealth '17: Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care

Pages 45 - 52

https://doi.org/10.1145/3132635.3132640

Published: 23 October 2017 Publication History

Abstract

Wearable sensor systems can deliver promising solutions to automatic monitoring of ingestive behavior. This study presents an on-body sensor system and related signal processing techniques to classify different types of food intake sounds. A piezoelectric throat microphone is used to capture food consumption sounds from the neck. The recorded signals are firstly segmented and decomposed using the empirical mode decomposition (EMD) analysis. EMD has been a widely implemented tool to analyze non-stationary and non-linear signals by decomposing data into a series of sub-band oscillations known as intrinsic mode functions (IMFs). For each decomposed IMF signal, time and frequency domain features are then computed to provide a multi-resolution representation of the signal. The minimum redundancy maximum relevance (mRMR) principle is utilized to investigate the most representative features for the food intake classification task, which is carried out using the support vector machines. Experimental evaluations over selected groups of features and EMD achieve significant performance improvements compared to the baseline classification system without EMD.

References

[1]

SM Shafiul Alam and Mohammed Imamul Hassan Bhuiyan. 2013. Detection of seizure and epilepsy using higher order statistics in the EMD domain. IEEE Journal of Biomedical and Health Informatics, Vol. 17, 2 (2013), 312--318.

[2]

Oliver Amft, Holger Junker, and Gerhard Troster. 2005. Detection of eating and drinking arm gestures using inertial body-worn sensors Wearable Computers, 2005. Proceedings. Ninth IEEE International Symposium on. IEEE, 160--163.

Digital Library

[3]

Michael A Crary, Jamie L Humphrey, Giselle Carnaby-Mann, Raam Sambandam, Leslie Miller, and Scott Silliman. 2013. Dysphagia, nutrition, and hydration in ischemic stroke patients at admission and discharge from acute care. Dysphagia, Vol. 28, 1 (2013), 69--76.

[4]

Ingrid Daubechies. 1988. Orthonormal bases of compactly supported wavelets. Communications on pure and applied mathematics, Vol. 41, 7 (1988), 909--996.

[5]

Chris Ding and Hanchuan Peng. 2005. Minimum redundancy feature selection from microarray gene expression data. Journal of bioinformatics and computational biology, Vol. 3, 02 (2005), 185--205.

[6]

Cumhur Ertekin, Nur Yüceyar, et almbox. 1998. Clinical and electrophysiological evaluation of dysphagia in myasthenia gravis. Journal of Neurology, Neurosurgery & Psychiatry, Vol. 65, 6 (1998), 848--856.

[7]

Patrick Flandrin, Gabriel Rilling, and Paulo Goncalves. 2004. Empirical mode decomposition as a filter bank. IEEE Signal Processing Letters Vol. 11, 2 (2004), 112--114.

[8]

Leela K Gudupudi, Navin Chatlani, Christophe Beaugeant, and Nicholas Evans. 2015. Non-linear acoustic echo cancellation using empirical mode decomposition Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference on. IEEE, 614--618.

[9]

Daniel Harari, Miriam Furst, Nahum Kiryati, Asaf Caspi, and Michael Davidson. 2001. A computer-based method for the assessment of body-image distortions in anorexia-nervosa patients. IEEE Transactions on Information Technology in Biomedicine, Vol. 5, 4 (2001), 311--319.

Digital Library

[10]

Norden E Huang, Zheng Shen, et almbox. 1998. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, Vol. Vol. 454. The Royal Society, 903--995.

[11]

Tal Klap and Zvika Shinar. 2013. Using piezoelectric sensor for continuous-contact-free monitoring of heart and respiration rates in real-life hospital settings Computing in Cardiology Conference (CinC), 2013. IEEE, 671--674.

[12]

M-È Labonté, A Cyr, L Baril-Gravel, MM Royer, and B Lamarche. 2012. Validity and reproducibility of a web-based, self-administered food frequency questionnaire. European journal of clinical nutrition Vol. 66, 2 (2012), 166--173.

[13]

David Lansky and Kelly D Brownell. 1982. Estimates of food quantity and calories: errors in self-report among obese patients. The American journal of clinical nutrition Vol. 35, 4 (1982), 727--732.

[14]

Danilo P Mandic, Naveed ur Rehman, Zhaohua Wu, and Norden E Huang. 2013. Empirical mode decomposition-based time-frequency analysis of multivariate signals: The power of adaptive data analysis. IEEE signal processing magazine Vol. 30, 6 (2013), 74--86.

[15]

Xuan Vinh Nguyen, Jeffrey Chan, Simone Romano, and James Bailey. 2014. Effective global approaches for mutual information based feature selection Proceedings of the international conference on knowledge discovery and data mining. ACM, 512--521.

Digital Library

[16]

World Health Organization. 2017. Obesity and overweight. (2017). http://www.who.int/mediacentre/factsheets/fs311/en {Accessed 6-March-2017}.

[17]

Edward S Sazonov and Juan M Fontana. 2012. A sensor system for automatic detection of food intake through non-invasive monitoring of chewing. IEEE sensors journal, Vol. 12, 5 (2012), 1340--1348.

[18]

M. A. Tuugtekin Turan and Engin Erzin. 2013. Enhancement of throat microphone recordings by learning phone-dependent mappings of speech spectra. In Acoustics, Speech and Signal Processing (ICASSP), IEEE International Conference on. IEEE, 7049--7053.

[19]

M. A. Tuugtekin Turan and Engin Erzin. 2016. Source and filter estimation for throat-microphone speech enhancement. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 24, 2 (2016), 265--275.

Digital Library

[20]

Luc F Van Gaal, Ilse L Mertens, and E Christophe. 2006. Mechanisms linking obesity with cardiovascular disease. Nature, Vol. 444, 7121 (2006), 875--880.

Cited By

Chen JWang XLi JChernyshov GHuang YKunze KHuang JStarner TZhang Q(2023)First Bite/Chew: distinguish different types of food by first biting/chewing and the corresponding hand movementExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585845(1-7)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544549.3585845
Chen XKamavuako E(2023)Detection of Fluid Intake Swallowing Events Using Acoustic Signals and Template Matching2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)10.1109/BIBE60311.2023.00072(403-408)Online publication date: 4-Dec-2023
https://doi.org/10.1109/BIBE60311.2023.00072
Ghosh TSazonov E(2023)Passive Sensors for Detection of Food IntakeEncyclopedia of Sensors and Biosensors10.1016/B978-0-12-822548-6.00086-8(218-234)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-822548-6.00086-8
Show More Cited By

Index Terms

Empirical Mode Decomposition of Throat Microphone Recordings for Intake Classification

Recommendations

Extreme-point weighted mode decomposition

A new signal decomposition method termed EWMD with two algorithms is proposed.A new IMF criterion closely related to its definition is established.The accuracy, decomposition capability and orthogonality of EWMD are studied.EWMD is compared with EMD by ...
A mean approximation based bidimensional empirical mode decomposition with application to image fusion

Empirical mode decomposition (EMD) is an adaptive decomposition method, which is widely used in time-frequency analysis. As a bidimensional extension of EMD, bidimensional empirical mode decomposition (BEMD) presents many useful applications in image ...
Empirical Mode Decomposition for Signal Preprocessing and Classification of Intrinsic Mode Functions

Empirical mode decomposition (EMD) is an adaptive, data-driven technique for processing and analyzing various types of non-stationary signals. EMD is a powerful and effective tool for signal preprocessing (denoising, detrending, regularity estimation) ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Conferences

MMHealth '17: Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care

October 2017

104 pages

ISBN:9781450355049

DOI:10.1145/3132635

Program Chairs:
Susanne Boll
University of Oldenburg, Germany
,
Touradj Ebrahimi
EPFL, Switzerland
,
Cathal Gurrin
Dublin City University, Ireland
,
Ramesh Jain
UC Irvine, USA
,
Laleh Jalali
Hitachi America Ltd. USA
,
Jochen Meyer
OFFIS-Institute for Information Technology, Germany
,
Noel O'Connor
Dublin City University, Ireland

Copyright © 2017 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

Sponsors

SIGMM: ACM Special Interest Group on Multimedia

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 23 October 2017

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Author Tags

Qualifiers

Research-article

Conference

MM '17

Sponsor:

SIGMM

MM '17: ACM Multimedia Conference

October 23, 2017

California, Mountain View, USA

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

6
Total Citations
View Citations
114
Total Downloads

Downloads (Last 12 months)1
Downloads (Last 6 weeks)0

Reflects downloads up to 09 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Chen JWang XLi JChernyshov GHuang YKunze KHuang JStarner TZhang Q(2023)First Bite/Chew: distinguish different types of food by first biting/chewing and the corresponding hand movementExtended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544549.3585845(1-7)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544549.3585845
Chen XKamavuako E(2023)Detection of Fluid Intake Swallowing Events Using Acoustic Signals and Template Matching2023 IEEE 23rd International Conference on Bioinformatics and Bioengineering (BIBE)10.1109/BIBE60311.2023.00072(403-408)Online publication date: 4-Dec-2023
https://doi.org/10.1109/BIBE60311.2023.00072
Ghosh TSazonov E(2023)Passive Sensors for Detection of Food IntakeEncyclopedia of Sensors and Biosensors10.1016/B978-0-12-822548-6.00086-8(218-234)Online publication date: 2023
https://doi.org/10.1016/B978-0-12-822548-6.00086-8
Lacanlale JIsayan PMkrtchyan KNahapetian A(2022)Sensoring the Neck: Classifying Movements and Actions with a Neck-Mounted Wearable DeviceSensors10.3390/s2212431322:12(4313)Online publication date: 7-Jun-2022
https://doi.org/10.3390/s22124313
Turan MErzin E(2021)Domain Adaptation for Food Intake Classification With Teacher/Student LearningIEEE Transactions on Multimedia10.1109/TMM.2020.303831523(4220-4231)Online publication date: 2021
https://doi.org/10.1109/TMM.2020.3038315
Mathew LPriya GGopakumar K(2021)Piezoelectric Throat Microphone Based Voice Analysis2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS)10.1109/ICACCS51430.2021.9441880(1603-1608)Online publication date: 19-Mar-2021
https://doi.org/10.1109/ICACCS51430.2021.9441880

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents