Bite Weight Prediction From Acoustic Recognition of Chewing
Open accessAbstract
Automatic dietary monitoring (ADM) offers new perspectives to reduce the self-reporting burden for participants in diet coaching programs. This paper 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 were analyzed for correlations between chewing behavior 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.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The text we found for this paper looks like another document, so the self-check wasn’t run.
Cites
Chiang, L. H., Russell, E. L., & Braatz, R. D. (2001) · Advanced textbooks in control and signal processing · doi:10.1007/978-1-4471-0347-9_3 · Cited by 5,053 · see citing works
Nonparametrics: Statistical Methods Based on Ranks
Mergenthaler, M. J., & Lehmann, E. L. (1979) · Technometrics · doi:10.2307/1268531 · Cited by 3,353 · see citing works
Pattern Classification (2nd ed.)
Duda, R. O., Hart, P. E., & Stork, D. G. (1999) · Cited by 2,509 · see citing works
Practical Nonparametric Statistics (2nd ed).
Groeneveld, R. A., & Conover, W. J. (1981) · Journal of the American Statistical Association · doi:10.2307/2287607 · Cited by 1,869 · see citing works
Practical Nonparametric Statistics
Bhapkar, V. P., & Conover, W. J. (1972) · Technometrics · doi:10.2307/1267149 · Cited by 1,422 · see citing works
(1990) · Choice Reviews Online · doi:10.5860/choice.27-5795 · Cited by 1,049 · see citing works
The validity of self-reported energy intake as determined using the doubly labelled water technique
Hill, R. J., & Davies, P. S. W. (2001) · British Journal of Nutrition · doi:10.1079/bjn2000281 · Cited by 589 · see citing works
Limitations in the assessment of dietary energy intake by self-report
Schoeller, D. A. (1995) · Metabolism · doi:10.1016/0026-0495(95)90204-x · Cited by 496 · see citing works
Gesture spotting with body-worn inertial sensors to detect user activities
Junker, H., Amft, O., Lukowicz, P., & Tröster, G. (2007) · Pattern Recognition · doi:10.1016/j.patcog.2007.11.016 · Cited by 361 · see citing works
Self-citationChanging the Energy Density of the Diet as a Strategy for Weight Management
Rolls, B. J., Drewnowski, A., & Ledikwe, J. H. (2005) · Journal of the American Dietetic Association · doi:10.1016/j.jada.2005.02.033 · Cited by 360 · see citing works
Adaptation of healthy mastication to factors pertaining to the individual or to the food.
Woda, A., Foster, K., Mishellany, A., & Peyron, M. (2006) · Physiology and Behavior · doi:10.1016/j.physbeh.2006.02.013 · Cited by 321 · see citing works
The influence of product and oral characteristics on swallowing
Engelen, L., Fontijn-Tekamp, A., & van der Bilt, A. (2005) · Archives of Oral Biology · doi:10.1016/j.archoralbio.2005.01.004 · Cited by 308 · see citing works
Validity of the assessment of dietary intake: problems of misreporting
Westerterp, K. R., & Goris, A. H. C. (2002) · Current Opinion in Clinical Nutrition & Metabolic Care · doi:10.1097/00075197-200209000-00006 · Cited by 279 · see citing works
Swallowing threshold and masticatory performance in dentate adults
Fontijn-Tekamp, F. A., van der Bilt, A., Abbink, J. H., et al. (2004) · Physiology & Behavior · doi:10.1016/j.physbeh.2004.08.026 · Cited by 225 · see citing works
Recognition of dietary activity events using on-body sensors
Amft, O., & Tröster, G. (2008) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2007.11.007 · Cited by 220 · see citing works
Self-citationAnalysis of Chewing Sounds for Dietary Monitoring
Amft, O., Stäger, M., Lukowicz, P., & Tröster, G. (2005) · Lecture Notes in Computer Science · doi:10.1007/11551201_4 · Cited by 216 · see citing works
Self-citationVariability of the masticatory process during chewing of elastic model foods
Lassauzay, C., Peyron, M., Albuisson, É., Dransfield, E., & Woda, A. (2000) · European Journal Of Oral Sciences · doi:10.1034/j.1600-0722.2000.00866.x · Cited by 214 · see citing works
On-Body Sensing Solutions for Automatic Dietary Monitoring
Amft, O., & Tröster, G. (2009) · IEEE Pervasive Computing · doi:10.1109/mprv.2009.32 · Cited by 175 · see citing works
Self-citationPeyron, M., Maskawi, K., Woda, A., et al. (1997) · Journal of Dental Research · doi:10.1177/00220345970760031201 · Cited by 143 · see citing works
Methods for Detection and Classification of Normal Swallowing from Muscle Activation and Sound
Amft, O., & Tröster, G. (2006) · doi:10.1109/pcthealth.2006.361624 · Cited by 100 · see citing works
Self-citationThe effects of bolus size and chewing rate on masticatory performance with artificial test foods
Buschang, P. H., Throckmorton, G. S., Travers, K. H., & JOHNSON, G. (1997) · Journal of Oral Rehabilitation · doi:10.1111/j.1365-2842.1997.tb00368.x · Cited by 91 · see citing works
Defining sensory descriptors: Towards writing guidelines based on terminology
Giboreau, A., Dacremont, C., Egoroff, C., et al. (2006) · Food Quality and Preference · doi:10.1016/j.foodqual.2005.12.003 · Cited by 84 · see citing works
The Materials Science of Eating and Food Breakdown
Lillford, P. J. (2000) · MRS Bulletin · doi:10.1557/mrs2000.252 · Cited by 83 · see citing works
Electromyographic responses to prescribed mastication
Kemsley, E. K., Defernez, M., Sprunt, J., et al. (2003) · Journal of Electromyography and Kinesiology · doi:10.1016/s1050-6411(02)00065-2 · Cited by 50 · see citing works
Assessing dietary intake in the management of obesity
O'Neil, P. M. (2001) · Obesity Research · doi:10.1002/oby.2001.9.s5.361 · Cited by 48 · see citing works
METHOD TO INVESTIGATE DIFFERENCES IN CHEWING BEHAVIOUR IN HUMANS
BROWN, W. E., SHEARN, M., & MacFie, H. J. H. (1994) · Journal of Texture Studies · doi:10.1111/j.1745-4603.1994.tb00752.x · Cited by 40 · see citing works
Effects of bolus size and hardness on within-subject variability of chewing cycle kinematics
Wintergerst, A. M., Throckmorton, G. S., & Buschang, P. H. (2008) · Archives of Oral Biology · doi:10.1016/j.archoralbio.2007.10.012 · Cited by 38 · see citing works
Orchardson, R., & Cadden, S. W. (1998) · KARGER eBooks · doi:10.1159/000061108 · Cited by 37 · see citing works
Microstructure of eating behavior in humans
Kissileff, H. R., & Guss, J. L. (2001) · Appetite · doi:10.1006/appe.2000.0369 · Cited by 30 · see citing works
Differences in chewing sounds of dry-crisp snacks by multivariate data analysis
Belie, N. D., Sivertsvik, M., & Baerdemaeker, J. D. (2003) · Journal of Sound and Vibration · doi:10.1016/s0022-460x(03)00589-3 · Cited by 27 · see citing works
Variables Influencing Chewing Electromyography Response in Food Texture Evaluation
González, R., Montoya, I., Benedito, J., et al. (2004) · Food Reviews International · doi:10.1081/fri-120028828 · Cited by 22 · see citing works
Significance Test for Feature Subset Selection on Image Recognition
Xu, Q., Kamel, M. S., & Salama, M. M. A. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-30125-7_31 · Cited by 20 · see citing works
“DELICIOUS PILAFA” APPLE DENSITY CHANGES AS A QUALITY INDEX OF MASS LOSS DEGRADATION DURING STORAGE
Mitropoulos, D., & Lambrinos, G. (2007) · Journal of Food Quality · doi:10.1111/j.1745-4557.2007.00140.x · Cited by 6 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
A review of wearable sensors and systems with application in rehabilitation
Patel, S., Park, H., Bonato, P., Chan, L., & Rodgers, M. (2012) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-9-21 · Cited by 2,396 · see citing works
Bonato, P. (2010) · IEEE Engineering in Medicine and Biology Magazine · doi:10.1109/memb.2010.936554 · Cited by 423 · see citing works
Audio-based detection and evaluation of eating behavior using the smartwatch platform
Kalantarian, H., & Sarrafzadeh, M. (2015) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2015.07.013 · Cited by 85 · see citing works
A wearable sensor system for medication adherence prediction
Kalantarian, H., Motamed, B., Alshurafa, N., & Sarrafzadeh, M. (2016) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2016.03.004 · Cited by 86 · see citing works
Artificial intelligence & clinical nutrition: What the future might have in store
Bond, A., Mccay, K., & Lal, S. (2023) · Clinical Nutrition ESPEN · doi:10.1016/j.clnesp.2023.07.082 · Cited by 79 · see citing works
iHear Food: Eating Detection Using Commodity Bluetooth Headsets
Gao, Y., Zhang, N., Wang, H., Ding, X., Ye, X., Chen, G., & Cao, Y. (2016) · 2016 IEEE First International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) · doi:10.1109/chase.2016.14 · Cited by 63 · see citing works
Bitey: an exploration of tooth click gestures for hands-free user interface control
Ashbrook, D., Tejada, C., Mehta, D., Jiminez, A., Muralitharam, G., Gajendra, S., & Tallents, R. (2016) · Proceedings of the 18th International Conference on Human-Computer Interaction with Mobile Devices and Services · doi:10.1145/2935334.2935389 · Cited by 49 · see citing works
Kouris, I., Mougiakakou, S., Scarnato, L., Iliopoulou, D., Diem, P., Vazeou, A., & Koutsouris, D. (2010) · International Journal of Electronic Healthcare · doi:10.1504/ijeh.2010.036209 · Cited by 46 · see citing works
- Authors
- Amft, O., Kusserow, M., & Troster, G.
- Affiliations
- Eindhoven University of Technology (NL)
- ETH Zurich (CH)
- ORCID
- Amft, Oliver
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2009-06
- Volume
- 56
- Issue
- 6
- Pages
- 1663-1672
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2144165269
- Cited by
- 123
- References
- 33
- Citations by year
- 2026: 3 · 2025: 3 · 2024: 5 · 2023: 6 · 2022: 12 · 2021: 12 · 2020: 12 · 2019: 8 · 2018: 4 · 2017: 16 · 2016: 12 · 2015: 8 · 2014: 5 · 2013: 4 · 2012: 4
- FWCI
- 4.78
- Type
- journal article
- Language
- en
- Open access
- green
- research.tue.nl
- Locations
- IEEE Transactions on Biomedical Engineering (publishedVersion)
- PubMed (publishedVersion)
- TU/e Research Portal (publishedVersion, other-oa)
- TU/e Research Portal (submittedVersion, other-oa)
- TU/e Research Portal (submittedVersion)
- Funders
- Eidgenössische Technische Hochschule Zürich
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Phonocardiography and Auscultation Techniques
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- acoustic sensors, chewing cycles, solid foods
- MeSH terms
- Acoustics, Adult, Algorithms, Electromyography, Feeding Behavior, Female, Food, Humans, Male, Mastication, Pattern Recognition, Automated, Signal Processing, Computer-Assisted, Reproducibility of Results, Monitoring, Ambulatory, Statistics, Nonparametric
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
