Authors:
Hao Zhang
;
Guillaume Lopez
;
Masaki Shuzo
;
Jean-Jacques Delaunay
and
Ichiro Yamada
Affiliation:
The University of Tokyo, Japan
Keyword(s):
Eating habits monitoring, Wearable sensor, Mastication counting, Chewing sound, Life-style diseases prevention.
Related
Ontology
Subjects/Areas/Topics:
Biomedical Engineering
;
Biomedical Signal Processing
;
Cardiovascular Technologies
;
Clinical Problems and Applications
;
Computing and Telecommunications in Cardiology
;
Development of Assistive Technology
;
Devices
;
Evaluation and Use of Healthcare IT
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Medical and Nursing Informatics
;
Physiological Computing Systems
;
Wearable Sensors and Systems
Abstract:
In recent years, an increasing number of people have been suffering from over-weight, reminding the importance of a balanced dietetic lifestyle. Researches in nutrition and oral health have reported that not only the calorie intake amount, but also eating speed and the number of chews per bite were also important factors in obesity. Automatic mastication counting systems based on chewing sound processing have been proposed, though most of them have difficulties in detecting chewing strokes for various food types, and often require training logic or threshold that need to be customized for each user. To overcome these problems, we have developed a new model for automatic mastication counting based on new chew feature extraction and detection methods from natural chewing sound. Chewing sounds collected from 15 persons eating six different food types were recorded using a wearable bone-conduction microphone placed in ear. The chewing sound analysis model combining proposed chew feature
extraction and detection methods was applied on the collected data set, showing a good overall accuracy while having better stability to different individuals and food types comparing to conventional models.
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