Food Intake Classification Using Throat Microphone. Turan, M. A. T. & Erzin, E. In 2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE (SIU), pages 1873-1876, 2016. IEEE; Bulent Ecevit Univ, Dept Elect & Elect Engn; Bulent Ecevit Univ, Dept Biomed Engn; Bulent Ecevit Univ, Dept Comp Engn. 24th Signal Processing and Communication Application Conference (SIU), Zonguldak, TURKEY, MAY 16-19, 2016
abstract   bibtex   
Swallowing action is one of the two fundamental elements of food intake mechanism. Classification of different swallowing patterns establishes an important part of the nutrient activity analysis. This paper is a preliminary research that investigates ingestion monitoring. We observe that throat microphone recordings can reveal certain characteristics of different swallowing types during food intake process. To evaluate the performance of proposed classifiers we recorded swallowing sounds regarding six different classes and extracted features over time-frequency analysis which results in between 60% and 80% accuracy. Experimental results are encouraging for automatic detection of swallowing events in future studies.
@inproceedings{ ISI:000391250900445,
Author = {Turan, M. A. Tugtekin and Erzin, Engin},
Book-Group-Author = {{IEEE}},
Title = {{Food Intake Classification Using Throat Microphone}},
Booktitle = {{2016 24TH SIGNAL PROCESSING AND COMMUNICATION APPLICATION CONFERENCE
   (SIU)}},
Year = {{2016}},
Pages = {{1873-1876}},
Note = {{24th Signal Processing and Communication Application Conference (SIU),
   Zonguldak, TURKEY, MAY 16-19, 2016}},
Organization = {{IEEE; Bulent Ecevit Univ, Dept Elect \& Elect Engn; Bulent Ecevit Univ,
   Dept Biomed Engn; Bulent Ecevit Univ, Dept Comp Engn}},
Abstract = {{Swallowing action is one of the two fundamental elements of food intake
   mechanism. Classification of different swallowing patterns establishes
   an important part of the nutrient activity analysis. This paper is a
   preliminary research that investigates ingestion monitoring. We observe
   that throat microphone recordings can reveal certain characteristics of
   different swallowing types during food intake process. To evaluate the
   performance of proposed classifiers we recorded swallowing sounds
   regarding six different classes and extracted features over
   time-frequency analysis which results in between 60\% and 80\% accuracy.
   Experimental results are encouraging for automatic detection of
   swallowing events in future studies.}},
ISBN = {{978-1-5090-1679-2}},
Unique-ID = {{ISI:000391250900445}},
}

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