TY  - JOUR
ID  - Amft2009-J_IEEETransBiomedEng
T1  - Bite weight prediction from acoustic recognition of chewing
A1  - Amft, Oliver
A1  - Kusserow, Martin
A1  - Tröster, Gerhard
JA  - IEEE Transactions on Biomedical Engineering
Y1  - 2009
VL  - 56
IS  - 6
SP  - 1663
EP  - 1672
M2  - doi: 10.1109/tbme.2009.2015873
N2  - 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.
ER  -