Architecting dietary intake monitoring as a service combining NLP and IoT Benítez-Guijarro, Antonio Callejas Carrión, Zoraida Noguera García, Manuel Benghazi, Kawtar natural language processing internet of things nutrition e-coach Currently 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. 2021-04-05T07:57:45Z 2021-04-05T07:57:45Z 2019-11-06 info:eu-repo/semantics/article Benítez-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/s12652-019-01553-2 http://hdl.handle.net/10481/67763 https://doi.org/10.1007/s12652-019-01553-2 eng info:eu-repo/grantAgreement/EC/H2020/823907 http://creativecommons.org/licenses/by-nc-nd/3.0/es/ info:eu-repo/semantics/openAccess Atribución-NoComercial-SinDerivadas 3.0 España