Bite Weight Prediction From Acoustic Recognition of Chewing

Automatic dietary monitoring (ADM) offers new perspectives to reduce the self-reporting burden for participants in diet coaching programs. This paper presents an approach to predict weight of individual bites taken. We utilize a pattern recognition procedure to spot chewing cycles and food type in c...

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Vydáno v:IEEE transactions on biomedical engineering Ročník 56; číslo 6; s. 1663 - 1672
Hlavní autoři: Amft, Oliver, Kusserow, Martin, Troster, Gerhard
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.06.2009
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Acoustic sensors
Acoustics
Adult
Algorithm implementation
Algorithms
biosignal processors
Electromyography
Feeding Behavior - physiology
Female
Food
Humans
Male
Mastication - physiology
Microstructure
Monitoring, Ambulatory - methods
Patient monitoring
Pattern recognition
Pattern Recognition, Automated - methods
Performance evaluation
Predictive models
Reproducibility of Results
Robustness
signal and image processing
Signal processing
Signal Processing, Computer-Assisted
Solids
Statistics, Nonparametric
Studies
Wearable computers
ISSN:0018-9294, 1558-2531, 1558-2531
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Shrnutí:Automatic dietary monitoring (ADM) offers new perspectives to reduce the self-reporting burden for participants in diet coaching programs. This paper presents an approach to predict weight of individual bites taken. We utilize a pattern recognition procedure to spot chewing cycles and food type in continuous data from an ear-pad chewing sound sensor. The recognized information is used to predict bite weight. We present our recognition procedure and demonstrate its operation on a set of three selected foods of different bite weights. Our evaluation is based on chewing sensor data of eight healthy study participants performing 504 habitual bites in total. The sound-based chewing recognition achieved recalls of 80% at 60%-70% precision. Food classification of chewing sequences resulted in an average accuracy of 94%. In total, 50 variables were derived from the chewing microstructure, and were analyzed for correlations between chewing behavior and bite weight. A subset of four variables was selected to predict bite weight using linear food-specific models. Mean weight prediction error was lowest for apples (19.4%) and largest for lettuce (31%) using the sound-based recognition. We conclude that bite weight prediction using acoustic chewing recordings is a feasible approach for solid foods, and should be further investigated.
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ISSN:0018-9294
1558-2531
1558-2531
DOI:10.1109/TBME.2009.2015873