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dc.contributor.authorBenítez-Guijarro, Antonio
dc.contributor.authorCallejas Carrión, Zoraida 
dc.contributor.authorNoguera García, Manuel 
dc.contributor.authorBenghazi, Kawtar
dc.date.accessioned2021-04-05T07:57:45Z
dc.date.available2021-04-05T07:57:45Z
dc.date.issued2019-11-06
dc.identifier.citationBenítez-Guijarro, A., Callejas, Z., Noguera, M. et al. Architecting dietary intake monitoring as a service combining NLP and IoT. J Ambient Intell Human Comput (2019). https://doi.org/10.1007/s12652-019-01553-2es_ES
dc.identifier.urihttp://hdl.handle.net/10481/67763
dc.description.abstractCurrently there exist many tools that support monitoring and encouragement of healthy nutrition habits in the context of wellness promotion. In this domain, interfaces based on natural language provide more flexibility for nutritional self-reporting than traditional form-based applications, allowing the users to provide richer and spontaneous descriptions. Nonetheless, in certain circumstances, natural language records may miss some important aspects, such as the quantity of food eaten, which results in incomplete recordings. In the Internet-of-Things (IoT) paradigm, smart home appliances can support and complement the recording process so as to make it more accurate. However, in order to build systems that support the semantic analysis of nutritional self-reports, it is necessary to integrate multiple inter-related components, possibly within complex e-health platforms. For this reason, these components should be designed and encapsulated avoiding monolithic approaches that derive in rigidity and dependency of particular technologies. Currently, there are no models or architectures that serve as a reference for developers towards this objective. In this paper, we present a service-based architecture that helps to contrast and complement the descriptions of food intakes by means of connected smart home devices, coordinating all the stages during the process of recognizing food records provided in natural language. Additionally, we aim to identify and design the essential services that are required to automate the recording and subsequent processing of natural language descriptions of nutritional intakes in association with smart home devices. The functionalities provided by each of these services are ready to work in isolation, just out of the box, or in downstream pipeline processes, bypassing the inconveniences of monolithic architectures.es_ES
dc.description.sponsorshipDEP2015-70980-R of the Spanish Ministry of Economy and Competitiveness (MINECO) and European Regional Development Fund (ERDF)es_ES
dc.description.sponsorshipEuropean Union’s Horizon 2020 research and innovation programme under grant agreement No 823907es_ES
dc.description.sponsorshipCOST Action IC1303 AAPELEes_ES
dc.language.isoenges_ES
dc.rightsAtribución-NoComercial-SinDerivadas 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/*
dc.subjectnatural language processinges_ES
dc.subjectinternet of thingses_ES
dc.subjectnutrition e-coaches_ES
dc.titleArchitecting dietary intake monitoring as a service combining NLP and IoTes_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/H2020/823907es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doihttps://doi.org/10.1007/s12652-019-01553-2
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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