| |  Online-Ressource |
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| Verfasst von: | Hellmers, Sandra [VerfasserIn]  |
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| | Izadpanah, Babak [VerfasserIn] |
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| | Elgert, Lena [VerfasserIn] |
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| | Diekmann, Rebecca [VerfasserIn] |
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| | Bauer, Jürgen M. [VerfasserIn] |
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| | Hein, Andreas [VerfasserIn] |
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| | Fudickar, Sebastian [VerfasserIn] |
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| Titel: | Towards an automated unsupervised mobility assessment for older people based on inertial TUG measurements |
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| Verf.angabe: | Sandra Hellmers, Babak Izadpanah, Lena Dasenbrock, Rebecca Diekmann, Jürgen M. Bauer, Andreas Hein and Sebastian Fudickar |
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| E-Jahr: | 2018 |
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| Jahr: | 2 October 2018 |
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| Umfang: | 17 S. |
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| Teil: | volume:18 |
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| | year:2018 |
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| | number:10 |
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| | extent:17 |
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| Fussnoten: | Published: 2 October 2018 ; Gesehen am 24.09.2019 |
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| Titel Quelle: | Enthalten in: Sensors |
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| Ort Quelle: | Basel : MDPI, 2001 |
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| Jahr Quelle: | 2018 |
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| Band/Heft Quelle: | Bd. 18 (2018), 10, Art.-Nr. 3310, insges. 17 S. |
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| ISSN Quelle: | 1424-8220 |
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| Abstract: | One of the most common assessments for the mobility of older people is the Timed Up and Go test (TUG). Due to its sensitivity regarding the indication of Parkinson’s disease (PD) or increased fall risk in elderly people, this assessment test becomes increasingly relevant, should be automated and should become applicable for unsupervised self-assessments to enable regular examinations of the functional status. With Inertial Measurement Units (IMU) being well suited for automated analyses, we evaluate an IMU-based analysis-system, which automatically detects the TUG execution via machine learning and calculates the test duration. as well as the duration of its single components. The complete TUG was classified with an accuracy of 96% via a rule-based model in a study with 157 participants aged over 70 years. A comparison between the TUG durations determined by IMU and criterion standard measurements (stopwatch and automated/ambient TUG (aTUG) system) showed significant correlations of 0.97 and 0.99, respectively. The classification of the instrumented TUG (iTUG)-components achieved accuracies over 96%, as well. Additionally, the system’s suitability for self-assessments was investigated within a semi-unsupervised situation where a similar movement sequence to the TUG was executed. This preliminary analysis confirmed that the self-selected speed correlates moderately with the speed in the test situation, but differed significantly from each other. |
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| DOI: | doi:10.3390/s18103310 |
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| URL: | Bitte beachten Sie: Dies ist ein Bibliographieeintrag. Ein Volltextzugriff für Mitglieder der Universität besteht hier nur, falls für die entsprechende Zeitschrift/den entsprechenden Sammelband ein Abonnement besteht oder es sich um einen OpenAccess-Titel handelt.
kostenfrei: Volltext ; Verlag ; Resolving-System: https://doi.org/10.3390/s18103310 |
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| | Volltext ; Verlag: https://doi.org/10.3390/s18103310 |
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| | Volltext: https://www.mdpi.com/1424-8220/18/10/3310 |
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| | DOI: https://doi.org/10.3390/s18103310 |
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| Datenträger: | Online-Ressource |
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| Sprache: | eng |
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| Sach-SW: | Domestic environment |
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| | Frailty |
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| | Functional decline |
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| | Geriatric assessment |
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| | IMU |
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| | Machine learning |
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| | Self-assessment |
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| | Semi-unsupervised |
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| | TUG |
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| | Wearable sensors |
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| K10plus-PPN: | 1677341904 |
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| Verknüpfungen: | → Zeitschrift |
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Towards an automated unsupervised mobility assessment for older people based on inertial TUG measurements / Hellmers, Sandra [VerfasserIn]; 2 October 2018 (Online-Ressource)
