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. 2022 Mar 29;22(7):2617.
doi: 10.3390/s22072617.

Multi-Device Nutrition Control

Carlos A S Cunha  1 , Rui P Duarte  1
Affiliations

Multi-Device Nutrition Control

Carlos A S Cunha et al. Sensors (Basel). .

Abstract

Precision nutrition is a popular eHealth topic among several groups, such as athletes, people with dementia, rare diseases, diabetes, and overweight. Its implementation demands tight nutrition control, starting with nutritionists who build up food plans for specific groups or individuals. Each person then follows the food plan by preparing meals and logging all food and water intake. However, the discipline demanded to follow food plans and log food intake results in high dropout rates. This article presents the concepts, requirements, and architecture of a solution that assists the nutritionist in building up and revising food plans and the user following them. It does so by minimizing human-computer interaction by integrating the nutritionist and user systems and introducing off-the-shelf IoT devices in the system, such as temperature sensors, smartwatches, smartphones, and smart bottles. An interaction time analysis using the keystroke-level model provides a baseline for comparison in future work addressing both the use of machine learning and IoT devices to reduce the interaction effort of users.

Keywords: IoT; food logging; food plans; machine learning; precision nutrition.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure A1
Figure A1
Nutritionist login.
Figure A2
Figure A2
Person registration.
Figure A3
Figure A3
Appointment creation.
Figure A4
Figure A4
Visualize food nutrients.
Figure A5
Figure A5
Import food table with nutrients.
Figure A6
Figure A6
Food plan creation.
Figure A7
Figure A7
Visualize food plan.
Figure A8
Figure A8
Log food and water intake.
Figure A9
Figure A9
Update water intake.
Figure A10
Figure A10
Visualize statistics.
Figure A11
Figure A11
Update periodic food entries and train for competition.
Figure A12
Figure A12
Change active food plan.
Figure A13
Figure A13
Connect watch API.
Figure A14
Figure A14
Provide Fitbit consent.
Figure 1
Figure 1
Nutritionist front-end. (a) Appointment. (b) Client details. (c) Food plan.
Figure 2
Figure 2
User front-end. (a) Daily meals. (b) Meal visualization. (c) Daily statistics.
Figure 3
Figure 3
Smartwatch. (a) Food plan visualization and logging. (b) Daily control of nutrients. (c) Daily control of water.
Figure 4
Figure 4
Architecture.
See this image and copyright information in PMC

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