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Oliver Amft, Martin Kusserow, and Gerhard Tröster. Bite weight prediction from acoustic recognition of chewing. IEEE Trans Biomed Eng, 56(6):1663–1672, June 2009.
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.
@ARTICLE{Amft2009-J_IEEETransBiomedEng,
author = {Oliver Amft and Martin Kusserow and Gerhard Tr\"oster},
title = {Bite weight prediction from acoustic recognition of chewing},
journal = {IEEE Trans Biomed Eng},
year = {2009},
volume = {56},
pages = {1663--1672},
number = {6},
month = {June},
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.},
doi = {10.1109/TBME.2009.2015873},
file = {Amft2009-J_IEEETransBiomedEng.pdf:Amft2009-J_IEEETransBiomedEng.pdf:PDF},
owner = {oam},
timestamp = {2009/01/24}
}
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