Abstract
The Functional Reach Test (FRT) is a commonly used clinical tool to evaluate the dynamic balance and fall risk in older adults and individuals with specific neurological conditions. Several studies have highlighted the importance of using FRT as a reliable and valid measure for assessing functional balance and fall risk in diverse populations. Additionally, FRT is sensitive to changes in balance function over time and can provide critical information for designing rehabilitation programs to improve balance and reduce the risk of falls. The FRT has also been used as a screening tool for identifying individuals who may benefit from further assessment or intervention. Thus, the FRT is a valuable clinical instrument for assessing functional balance and fall risk and should be incorporated into routine clinical practice. This paper intends to describe the preliminary results and future directions for implementing the FRT with various sensors gathered from smartphones or smart wearables to provide valuable indicators to aid professional healthcare practitioners in evaluating and following up on the elderly but possibly extending to other age groups.
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Acknowledgments
This work is funded by FCT/MEC through national funds and co-funded by FEDER – PT2020 partnership agreement under the project UIDB/00308/2020.
This work is funded by FCT/MEC through national funds and co-funded by FEDER–PT2020 partnership agreement under the project UIDB/50008/2020.
This article is based upon work from COST Action CA18119 (Who Cares in Europe?), COST Action CA21118 Platform Work Inclusion Living Lab (P-WILL), and COST Action CA19101 Determinants of Physical Activities in Settings (DE-PASS). More information on www.cost.eu.
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Duarte, J., Francisco, L., Pires, I.M., Coelho, P.J. (2023). Smart Wearables Data Collection and Analysis for Medical Applications: A Preliminary Approach for Functional Reach Test. In: Rojas, I., Valenzuela, O., Rojas Ruiz, F., Herrera, L.J., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2023. Lecture Notes in Computer Science(), vol 13920. Springer, Cham. https://doi.org/10.1007/978-3-031-34960-7_34
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