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Resumo(s)
Precision nutrition is a popular eHealth topic among several groups, such as athletes, 1
people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight 2
nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. 3
Each person then follows the food plan by preparing meals and logging all food and water intake. 4
However, the discipline demanded to follow food plans and log food intake turns out into high 5
dropout rates. This article presents the concepts, requirements, and architecture of a solution that 6
assists the nutritionist in building up and revising food plans and the user following them. It does 7
so by minimizing human-computer interaction by integrating the nutritionist and user systems 8
and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, 9
smartphones, and smart bottles. An interaction time analysis using the Keystroke Level Model 10
provides a baseline for comparison in future work addressing both the use of machine learning and 11
IoT devices to reduce the interaction effort of users.
Descrição
Palavras-chave
Precision Nutrition Food plans IoT Machine Learning Food Logging
Contexto Educativo
Citação
Cunha, C. A. S., & Duarte, R. P. (2022). Multi-Device Nutrition Control. Sensors, 22(7). https://doi.org/10.3390/s22072617