Publication
Multi-Device Nutrition Control
| dc.contributor.author | Cunha, Carlos A. S. | |
| dc.contributor.author | P. Duarte, Rui | |
| dc.date.accessioned | 2023-06-26T09:14:23Z | |
| dc.date.available | 2023-06-26T09:14:23Z | |
| dc.date.issued | 2022 | |
| dc.description.abstract | 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. | pt_PT |
| dc.description.version | info:eu-repo/semantics/publishedVersion | pt_PT |
| dc.identifier.citation | Cunha, C. A. S., & Duarte, R. P. (2022). Multi-Device Nutrition Control. Sensors, 22(7). https://doi.org/10.3390/s22072617 | pt_PT |
| dc.identifier.doi | 10.3390/s22072617 | pt_PT |
| dc.identifier.eissn | 1424-8220 | |
| dc.identifier.uri | http://hdl.handle.net/10400.19/7820 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.relation | This work is funded by National Funds through the FCT - Foundation for Science and 552 Technology, I.P., within the scope of the project Ref. UIDB/05583/2020 | pt_PT |
| dc.subject | Precision Nutrition | pt_PT |
| dc.subject | Food plans | pt_PT |
| dc.subject | IoT | pt_PT |
| dc.subject | Machine Learning | pt_PT |
| dc.subject | Food Logging | pt_PT |
| dc.title | Multi-Device Nutrition Control | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.issue | 7 | pt_PT |
| oaire.citation.startPage | 2617 | pt_PT |
| oaire.citation.title | Sensors | pt_PT |
| oaire.citation.volume | 22 | pt_PT |
| person.familyName | Monteiro Amaro Duarte | |
| person.givenName | Rui Pedro | |
| person.identifier | gIYE8M4AAAAJ | |
| person.identifier.ciencia-id | 211F-55A0-4B63 | |
| person.identifier.orcid | 0000-0002-6819-0985 | |
| person.identifier.scopus-author-id | 14059938600 | |
| rcaap.rights | openAccess | pt_PT |
| rcaap.type | article | pt_PT |
| relation.isAuthorOfPublication | d56c3162-80a4-4ade-810d-43bae4ee6d73 | |
| relation.isAuthorOfPublication.latestForDiscovery | d56c3162-80a4-4ade-810d-43bae4ee6d73 |