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Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing

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dc.contributor.authorLopez-Meyer, Paulo
dc.contributor.authorSchuckers, Stephanie
dc.contributor.authorMakeyev, Oleksandr
dc.contributor.authorFontana, Juan M.
dc.contributor.authorSazonov, Edward
dc.contributor.otherUniversity of Alabama Tuscaloosa
dc.contributor.otherClarkson University
dc.contributor.otherUniversity of Rhode Island
dc.date.accessioned2023-09-28T19:11:32Z
dc.date.available2023-09-28T19:11:32Z
dc.date.issued2012
dc.description.abstractThe number of distinct foods consumed in a meal is of significant clinical concern in the study of obesity and other eating disorders. This paper proposes the use of information contained in chewing and swallowing sequences for meal segmentation by food types. Data collected from experiments of 17 volunteers were analyzed using two different clustering techniques. First, an unsupervised clustering technique, Affinity Propagation (AP), was used to automatically identify the number of segments within a meal. Second, performance of the unsupervised AP method was compared to a supervised learning approach based on Agglomerative Hierarchical Clustering (AHC). While the AP method was able to obtain 90% accuracy in predicting the number of food items, the AHC achieved an accuracy >95%. Experimental results suggest that the proposed models of automatic meal segmentation may be utilized as part of an integral application for objective Monitoring of Ingestive Behavior in free living conditions. (C) 2011 Elsevier Ltd. All rights reserved.en_US
dc.format.mediumelectronic
dc.format.mimetypeapplication/pdf
dc.identifier.citationLopez-Meyer, P., Schuckers, S., Makeyev, O., Fontana, J. M., & Sazonov, E. (2012). Automatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewing. In Biomedical Signal Processing and Control (Vol. 7, Issue 5, pp. 474–480). Elsevier BV. https://doi.org/10.1016/j.bspc.2011.11.004
dc.identifier.doi10.1016/j.bspc.2011.11.004
dc.identifier.orcidhttps://orcid.org/0000-0002-9365-9642
dc.identifier.orcidhttps://orcid.org/0000-0001-7792-4234
dc.identifier.orcidhttps://orcid.org/0000-0002-8934-1359
dc.identifier.orcidhttps://orcid.org/0000-0003-2648-0500
dc.identifier.urihttps://ir.ua.edu/handle/123456789/11001
dc.languageEnglish
dc.language.isoen_US
dc.publisherElsevier
dc.subjectMonitoring of Ingestive Behavior
dc.subjectFood intake
dc.subjectClustering
dc.subjectAffinity Propagation
dc.subjectAgglomerative Hierarchical Clustering
dc.subjectENERGY-INTAKE
dc.subjectINGESTIVE BEHAVIOR
dc.subjectOBJECTIVE QUANTIFICATION
dc.subjectANOREXIA-NERVOSA
dc.subjectEATING BEHAVIOR
dc.subjectDIET
dc.subjectHUMANS
dc.subjectPOPULATION
dc.subjectFREQUENCY
dc.subjectMARKERS
dc.subjectEngineering, Biomedical
dc.titleAutomatic identification of the number of food items in a meal using clustering techniques based on the monitoring of swallowing and chewingen_US
dc.typeArticle
dc.typetext

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