Toward Objective Monitoring of Ingestive Behavior in Free‐living Population
Open accessAbstract
Understanding of eating behaviors associated with obesity requires objective and accurate monitoring of food intake patterns. Accurate methods are available for measuring total energy expenditure and its components in free‐living populations, but methods for measuring food intake in free‐living people are far less accurate and involve self‐reporting or subjective monitoring. We suggest that chews and swallows can be used for objective monitoring of ingestive behavior. This hypothesis was verified in a human study involving 20 subjects. Chews and swallows were captured during periods of quiet resting, talking, and meals of varying size. The counts of chews and swallows along with other derived metrics were used to build prediction models for detection of food intake, differentiation between liquids and solids, and for estimation of the mass of ingested food. The proposed prediction models were able to detect periods of food intake with >95% accuracy and a fine time resolution of 30 s, differentiate solid foods from liquids with >91% accuracy, and predict mass of ingested food with >91% accuracy for solids and >83% accuracy for liquids. In earlier publications, we have shown that chews and swallows can be captured by noninvasive sensors that could be developed into a wearable device. Thus, the proposed methodology could lead to the development of an innovative new way of assessing human eating behavior in free‐living conditions.
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Wearable technology has been used to monitor physical activities in obese individuals and to facilitate clinical interventions based on encouraging an active and healthy lifestyle.
“For example, wearable technology has been used to monitor physical activities in obese individuals and to facilitate the implementation of clinical interventions based on encouraging an active and healthy lifestyle [53-56].”
From A review of wearable sensors and systems with application in rehabilitation (2012) · cites this paper as [56]
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Papapanagiotou, V., Diou, C., Zhou, L., van den Boer, J., Mars, M., & Delopoulos, A. N. (2016) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2016.2625271 · Cited by 91 · see citing works
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“Researchers have claimed that the information extracted from the temporal sequence of chews and swallows can be used to estimate the mass of food consumed [29].”
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“Swallows were also utilized in [4], [11], [26], [41], [46] to monitor ingestive behavior and estimate the amount of food.”
MethodLopez‐Meyer, P., Schuckers, S., Makeyev, O., Fontana, J. M., & Sazonov, E. E. (2011) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2011.11.004 · Cited by 21 · see citing works
“…validated the reliability of the produced manual scores [8–10], established a methodology of automatic detection of swallowing instances by acoustical means [11], and proposed and validated several methods for detection and characterization of food intake based on chewing and swallowing [10,12].”
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Self-citationDetection and Characterization of Food Intake by Wearable Sensors
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“…aim to compare the methods employed here with 24-hour/day automatic meal registration methods, such as the ones developed by Sazonov et al [40], Sun et al [41], and Kyritsis et al [42], which have the potential to remove recall and social desirability bias (eg, where individuals only…”
Automated Ingestion Detection for a Health Monitoring System
Walker, W., & Bhatia, D. K. (2013) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2013.2279193 · Cited by 18 · see citing works
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“In [5], both swallowing and chewing detection were utilized using a sound sensor located over the laryngopharynx and a below-the-ear strain sensor.”
MethodSelf-citationEvaluation of Chewing and Swallowing Sensors for Monitoring Ingestive Behavior
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Self-citationIndividual differences in the forced swimming test and neurochemical kinetics in the rat brain
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“Beyond dysphagia, measuring swallowing frequency is of interest to monitor food intake to help control overeating [7].”
BackgroundTowards automated ingestion detection: Swallow sounds
Walker, W., & Bhatia, D. K. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6091788 · Cited by 13 · see citing works
“[17] [18] [19] used both a piezoelectric strain sensor and a microphone to detect the event of ingestion and an estimate of the type of ingested bolus.”
MethodUsing respiratory signals for the recognition of human activities
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“Moreover, in the case of smoking, breathing segments start with an apnea, and previous studies have shown that these apneas can occur any time a subject swallows [27].”
BackgroundSelf-citationFrom Enabling Technology to Clinical Applications
Bonato, P. (2010) · Cited by 12 · see citing works
Detection and characterization of food intake by wearable sensors
Fontana, J. M., Farooq, M., & Sazonov, E. E. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819246-7.00020-6 · Cited by 11 · see citing works
Self-citationDesign of a instrumentation module for monitoring ingestive behavior in laboratory studies
Fontana, J. M., Lopez‐Meyer, P., & Sazonov, E. E. (2011) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/iembs.2011.6090534 · Cited by 9 · see citing works
“estimate the weight of ingested food [12]-[14].”
Self-citationThe “Smart Dining Table”: Automatic Behavioral Tracking of a Meal with a Multi-Touch-Computer
Manton, S., Magerowski, G., Patriarca, L., & Alonso‐Alonso, M. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00142 · Cited by 8 · see citing works
Recognizing cigarette smoke inhalations using hidden Markov models
Ramos-Garcia, R. I., Sazonov, E. E., & Tiffany, S. T. (2017) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2017.8037056 · Cited by 6 · see citing works
Self-citationBite count rates in free-living individuals: new insights from a portable sensor
Alex, J., Turner, D. S., Thomas, D. M., McDougall, A., Halawani, M., Heymsfield, S. B., Martin, C. K., Scisco, J. L., Salley, J. N., Muth, E. R., et al. (2018) · BMC Nutrition · doi:10.1186/s40795-018-0227-x · Cited by 6 · see citing works
“These results are concordant with conclusions derived from a chewing and swallowing sensor, which found that there appeared to be no correlation between mass per chew/swallow and BMI [1].”
ResultWiEat: Fine-grained Device-free Eating Monitoring Leveraging Wi-Fi SignalsPreprint
Wang, C., Lin, Z., Xie, Y., Guo, X., Ren, Y., & Chen, Y. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.09096 · Cited by 6 · see citing works
“swallows are two critical measurements for quantifying the food intake amount [33], [34].”
BackgroundSensors and systems for obesity care and research
Jovanov, E., Sazonov, E. E., & Poon, C. C. Y. (2014) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2014.6944300 · Cited by 5 · see citing works
Self-citationA Deep-Learning-Based Method of Estimating Water Intake
Yamada, Y., Saito, T., Kawasaki, S., Ikeda, D., Katagiri, M., Nishimura, M., & Mineno, H. (2017) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac.2017.14 · Cited by 5 · see citing works
“For example, extracting time and energy information from swallowing sounds, classifying and detecting coughs and swallowing behavior [11], and classifying feeding, swallowing, and utterance behaviors by using swallowing and chewing densities per section research [12] have been studied.”
sEMG-based automatic characterization of swallowed materials
Hassan, E. A., Khalifa, Y., & Morsy, A. A. (2024) · BioMedical Engineering OnLine · doi:10.1186/s12938-024-01241-z · Cited by 4 · see citing works
“For instance, swallowing sounds collected from a throat microphone and conduction microphone placed on the mastoid bone along with a strain sensor have been used for monitoring chewing and swallowing activities and determining the bolus nature being composed of solids or liquids [13, 14].”
MethodReview of monitoring devices for food intake
TAMURA, T., & Kimura, Y. (2016) · doi:10.11546/cicsj.34.73 · Cited by 4 · see citing works
Monitoring and evaluation of ingestive activities
Hassan, E. A., Elbially, M. S., & Morsy, A. A. (2018) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi.2018.8333360 · Cited by 4 · see citing works
Man or machine? Will the digital transition be able to automatize dietary intake data collection?
Mikkelsen, B. E. (2019) · Public Health Nutrition · doi:10.1017/s1368980018003993 · Cited by 4 · see citing works
Detecting Periods of Eating in Everyday Life by Tracking Wrist Motion — What is a Meal?
Sharma, S. P. (2020) · TigerPrints (Clemson University) · Cited by 3 · see citing works
Automatic Recognition of Food Ingestion Environment from the AIM-2 Wearable Sensor
Huang, Y., Hassan, M. A., He, J., HIGGINS, J. A., McCrory, M. A., Eicher‐Miller, H. A., Thomas, J. G., Sazonov, E. E., & Zhu, F. M. (2024) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw63382.2024.00372 · Cited by 2 · see citing works
“Most recent dietary assessment research mainly focuses on monitoring the intake of energy and nutrients in individuals’ diets [17, 22, 44, 47].”
Self-citationA Novel Approach to Calculating Energy Density from Food Images Reduces Analysis Time and Cost
Pan, Z., Marden, T. J., Mande, A., & HIGGINS, J. A. (2019) · Food and Nutrition Sciences · doi:10.4236/fns.2019.102018 · Cited by 2 · see citing works
Swallow sound analysis for automated ingestion detection
Walker, W., & Bhatia, D. K. (2011) · International Conference on PErvasive Technologies Related to Assistive Environments (PETRA) · doi:10.1145/2141622.2141649 · Cited by 2 · see citing works
“[20] [21] used both a piezoelectric strain sensor and a microphone to detect the event of ingestion and an estimate of the type of ingested bolus.”
BackgroundMethodUsing Hidden Markov Models to Segment and Classify Wrist Motions Related to Eating Activities
García, R., & Ignacio, R. (2014) · Cited by 2 · see citing works
Automatic Estimation of Fluid Volume Intake
Hassan, E. A., & Morsy, A. A. (2021) · Lecture notes in networks and systems · doi:10.1007/978-3-030-82199-9_35 · Cited by 2 · see citing works
Reyes, J. J. (2014) · TigerPrints (Clemson University) · Cited by 2 · see citing works
Orientation-Based Food Image Capture for Head Mounted Egocentric Camera
Hassan, M. A., & Sazonov, E. E. (2019) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc.2019.8857078 · Cited by 1 · see citing works
“The rise in diabetes, obesity and cardiovascular diseases due to poor diet have stirred a rapid development in ingestion monitoring [1].”
BackgroundSelf-citationHerrera, M. C. A. (2021) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r3-m8dy-7676
Wearable Devices for Injury Prevention and Rehabilitation
Mittal, V., Upadhyay, P., & Sharma, A. (2025) · Wiley eBooks · doi:10.1002/9781394302895.ch11
Empirical Evaluation of the Mass Balance Model in Human Bodyweight RegulationPreprint
Arencibia‐Albite, F. (2026) · Research Square · doi:10.21203/rs.3.rs-8576437/v1
- Authors
- Sazonov, E. S., Schuckers, S. A. C., Lopez‐Meyer, P., Makeyev, O., Melanson, E. L., Neuman, M. R., & Hill, J. O.
- Affiliations
- Clarkson University (US)
- University of Colorado Denver (US)
- Michigan Technological University (US)
- Venue
- Obesity
- Publisher
- Wiley
- Published
- 2009-10
- Volume
- 17
- Issue
- 10
- Pages
- 1971-1975
- ISSN
- 1930-7381 (ISSN-L)
- 1930-739X
- PMC
- PMC2865137
- OpenAlex
- W2012656506
- Cited by
- 74
- References
- 28
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 3 · 2022: 2 · 2021: 2 · 2020: 5 · 2019: 8 · 2018: 8 · 2017: 5 · 2016: 7 · 2014: 9 · 2013: 4 · 2012: 4
- FWCI
- 6.96
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Obesity (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Digital Commons - Michigan Tech (Michigan Technological University) (submittedVersion)
- Funders
- National Institutes of Health
- National Heart, Lung, and Blood Institute (R21HL083052)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Context-Aware Activity Recognition Systems
- Health Sciences › Nursing › Nutrition and Dietetics › Nutrition, Health and Food Behavior
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- ingestive behavior, dietary monitoring, swallowing, dietary habits, wearable sensors, non-invasive sensing, free-living population
- MeSH terms
- Deglutition, Eating, Feeding Behavior, Female, Humans, Male, Mastication, Models, Biological
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.
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