| 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 |
| Full Text |
PDF |
