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Thought on food: a systematic review of current approaches and challenges for food intake detection

2022Journal article Open access
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dc.contributor.authorNeves, Paulo Alexandre
dc.contributor.authorSimões, João
dc.contributor.authorCosta, Ricardo
dc.contributor.authorPimenta, Luís
dc.contributor.authorGonçalves, Norberto Jorge
dc.contributor.authorAlbuquerque, Carlos
dc.contributor.authorCunha, Carlos
dc.contributor.authorZdravevski, Eftim
dc.contributor.authorLameski, Petre
dc.contributor.authorGarcia, Nuno M.
dc.contributor.authorPires, Ivan M.
dc.date.accessioned2022-09-15T12:24:23Z
dc.date.available2022-09-15T12:24:23Z
dc.date.issued2022
dc.description.abstractNowadays, individuals have very stressful lifestyles, affecting their nutritional habits. In the early stages of life, teenagers begin to exhibit bad habits and inadequate nutrition. Likewise, other people with dementia, Alzheimer’s disease, or other conditions may not take food or medicine regularly. Therefore, the ability to monitor could be beneficial for them and for the doctors that can analyze the patterns of eating habits and their correlation with overall health. Many sensors help accurately detect food intake episodes, including electrogastrography, cameras, microphones, and inertial sensors. Accurate detection may provide better control to enable healthy nutrition habits. This paper presents a systematic review of the use of technology for food intake detection, focusing on the different sensors and methodologies used. The search was performed with a Natural Language Processing (NLP) framework that helps screen irrelevant studies while following the PRISMA methodology. It automatically searched and filtered the research studies in different databases, including PubMed, Springer, ACM, IEEE Xplore, MDPI, and Elsevier. Then, the manual analysis selected 30 papers based on the results of the framework for further analysis, which support the interest in using sensors for food intake detection and nutrition assessment. The mainly used sensors are cameras, inertial, and acoustic sensors that handle the recognition of food intake episodes with artificial intelligence techniques. This research identifies the most used sensors and data processing methodologies to detect food intake.pt_PT
dc.description.sponsorshipCOST Action IC1303-AAPELE—Architectures, Algorithms, and Protocols for Enhanced Living Environments and COST Action CA16226–SHELD-ON—Indoor living space improvement: Smart Habitat for the Elderly, supported by COST (European Cooperation in Science and Technology)
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.citationNEVES, P.A. [et al.] (2022) - Thought on food : a systematic review of current approaches and challenges for food intake detection. Sensors. Vol. 22, nº.17, p. 6443. DOI: 10.3390/s22176443.pt_PT
dc.identifier.doi10.3390/s22176443pt_PT
dc.identifier.urihttp://hdl.handle.net/10400.11/8121
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherMDPIpt_PT
dc.relation.publisherversionhttps://www.mdpi.com/1424-8220/22/17/6443pt_PT
dc.subjectFood intake Detectionpt_PT
dc.subjectbiosensorspt_PT
dc.subjectneural networkspt_PT
dc.subjectimage processingpt_PT
dc.subjectnutritionpt_PT
dc.titleThought on food: a systematic review of current approaches and challenges for food intake detectionpt_PT
dc.typejournal article
dspace.entity.typePublication
oaire.citation.issue17pt_PT
oaire.citation.startPage6443pt_PT
oaire.citation.titleSensorspt_PT
oaire.citation.volume22pt_PT
person.familyNameNeves
person.familyNameGarcia dos Santos
person.familyNameSerrano Pires
person.givenNamePaulo Alexandre
person.givenNameNuno Manuel
person.givenNameIvan Miguel
person.identifier.ciencia-idE719-0DEC-9751
person.identifier.ciencia-id211D-8B3D-0131
person.identifier.orcid0000-0002-7958-973X
person.identifier.orcid0000-0002-3195-3168
person.identifier.orcid0000-0002-3394-6762
rcaap.rightsopenAccesspt_PT
rcaap.typearticlept_PT
relation.isAuthorOfPublicationc90b1bfb-be87-49f0-ae6e-f149a11f9009
relation.isAuthorOfPublicationc3c2a619-5f2a-42ae-8ee2-f21b9c42a33a
relation.isAuthorOfPublication53c92dfa-27be-4305-9b32-f3cf2e36a4f1
relation.isAuthorOfPublication.latestForDiscoveryc90b1bfb-be87-49f0-ae6e-f149a11f9009

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