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, 2016abstract 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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