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@ARTICLE{Amft2009-J_IEEETransBiomedEng,
    author = {Amft, Oliver and Kusserow, Martin and Tr{\"{o}}ster, Gerhard},
     month = {June},
     title = {Bite weight prediction from acoustic recognition of chewing},
   journal = {IEEE Transactions on Biomedical Engineering},
    volume = {56},
    number = {6},
      year = {2009},
     pages = {1663--1672},
       doi = {10.1109/tbme.2009.2015873},
  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.}
}