Eintrag weiter verarbeiten
Verfügbar über Online-Ressource

Towards an automated unsupervised mobility assessment for older people based on inertial TUG measurements

Gespeichert in:

Veröffentlicht in: Sensors Bd. 18 (2018), 10, Art.-Nr. 3310, insges. 17 S.
Personen und Körperschaften: Hellmers, Sandra (VerfasserIn), Izadpanah, Babak (VerfasserIn), Elgert, Lena (VerfasserIn), Diekmann, Rebecca (VerfasserIn), Bauer, Jürgen M. (VerfasserIn), Hein, Andreas (VerfasserIn), Fudickar, Sebastian (VerfasserIn)
Titel: Towards an automated unsupervised mobility assessment for older people based on inertial TUG measurements/ Sandra Hellmers, Babak Izadpanah, Lena Dasenbrock, Rebecca Diekmann, Jürgen M. Bauer, Andreas Hein and Sebastian Fudickar
Format: E-Book-Kapitel
Sprache: Englisch
veröffentlicht:
2 October 2018
Gesamtaufnahme: : Sensors, Bd. 18 (2018), 10, Art.-Nr. 3310, insges. 17 S.
, volume:18
Schlagwörter:
Domestic environment
Frailty
Functional decline
Geriatric assessment
IMU
Machine learning
Self-assessment
Semi-unsupervised
TUG
Wearable sensors
Quelle: Verbunddaten SWB
Lizenzfreie Online-Ressourcen
Details
Zusammenfassung: 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.
Beschreibung: Published: 2 October 2018
Gesehen am 24.09.2019
Umfang: 17
ISSN: 1424-8220
DOI: 10.3390/s18103310