Bite weight prediction from acoustic recognition of chewing

Publication Type Journal Article
Authors Oliver Amft, Martin Kusserow, Gerhard Tröster
Title Bite weight prediction from acoustic recognition of chewing
Abstract Automatic Dietary Monitoring (ADM) offers new perspectives to reduce the self-reporting burden for participants in diet coaching programs. This work 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 analyzed for correlations between chewing behaviour 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.
Publication IEEE Transactions on Biomedical Engineering
Volume 56
Issue 6
Pages 1663–1672
Date June 2009
DOI 10.1109/TBME.2009.2015873
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Friedrich-Alexander-Universität Erlangen-Nürnberg