Please use this identifier to cite or link to this item:
http://hdl.handle.net/10773/41641
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DC Field | Value | Language |
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dc.contributor.author | Matos, Tomás | pt_PT |
dc.contributor.author | Vornicoglo, Daniel | pt_PT |
dc.contributor.author | Coelho, Paulo Jorge | pt_PT |
dc.contributor.author | Zdravevski, Eftim | pt_PT |
dc.contributor.author | Albuquerque, Carlos | pt_PT |
dc.contributor.author | Pires, Ivan Miguel | pt_PT |
dc.date.accessioned | 2024-04-19T16:45:51Z | - |
dc.date.available | 2024-04-19T16:45:51Z | - |
dc.date.issued | 2024-02 | - |
dc.identifier.uri | http://hdl.handle.net/10773/41641 | - |
dc.description.abstract | There is growing interest in the automated measurement of physical fitness tests, such as the Arm Curl Test, to enable more objective and accurate assessments. This review aimed to systematically analyze the types of sensors and technological methods used for automated Arm Curl Test measurement and their benefits for different populations. The search consisted of the search related to the possibilities to measure the Arm Curl Test results with sensors in scientific databases, including PubMed Central, IEEE Explore, Elsevier, Springer, MDPI, ACM, and PMC, published from January 2010 to October 2022. The analysis included 30 studies from 15 nations with diverse populations analyzed. According to data extraction, the most prevalent sensors were chronometers, accelerometers, stadiometers, and dynamometers. In the investigations, statistical analysis predominated. The study shows how automated sensor technologies can objectively measure the Arm Curl Test. The detected sensors combined with statistical analysis techniques can enhance assessments. Applications for the Arm Curl Test may be improved even more with more research on cutting-edge sensors and algorithms. This evaluation offers insightful information about utilizing sensor-based automation to enhance Arm Curl Testing. | pt_PT |
dc.language.iso | eng | pt_PT |
dc.publisher | Springer | pt_PT |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F50008%2F2020/PT | pt_PT |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00308%2F2020/PT | pt_PT |
dc.relation | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F00742%2F2020/PT | pt_PT |
dc.rights | openAccess | pt_PT |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | pt_PT |
dc.subject | Arm Curl Test | pt_PT |
dc.subject | IoT | pt_PT |
dc.subject | Systematic review | pt_PT |
dc.subject | Mobile devices | pt_PT |
dc.subject | Telemedicine | pt_PT |
dc.subject | Sensors | pt_PT |
dc.title | Can sensors be used to measure the Arm Curl Test results? a systematic review | pt_PT |
dc.type | article | pt_PT |
dc.description.version | published | pt_PT |
dc.peerreviewed | yes | pt_PT |
degois.publication.issue | 2 | pt_PT |
degois.publication.title | Discover Applied Sciences | pt_PT |
degois.publication.volume | 6 | pt_PT |
dc.identifier.doi | 10.1007/s42452-024-05643-5 | pt_PT |
dc.identifier.essn | 3004-9261 | pt_PT |
dc.identifier.articlenumber | 48 | pt_PT |
Appears in Collections: | ESTGA - Artigos IT - Artigos |
Files in This Item:
File | Description | Size | Format | |
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s42452-024-05643-5.pdf | 1.41 MB | Adobe PDF | View/Open |
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