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Authors
Advisor(s)
Abstract(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.
Description
Keywords
Precision Nutrition Food plans IoT Machine Learning Food Logging
Citation
Cunha, C. A. S., & Duarte, R. P. (2022). Multi-Device Nutrition Control. Sensors, 22(7). https://doi.org/10.3390/s22072617
