Hellmers, Sandra and Izadpanah, Babak and Dasenbrock, Lena and Diekmann, Rebecca and Bauer, Jürgen M. and Hein, Andreas and Fudickar, Sebastian (2018) Towards an automated unsupervised mobility assessment for older people based on inertial TUG measurements. Sensors, 18 (10). p. 3310. ISSN 1424-8220

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Official URL: http://dx.doi.org/10.3390/s18103310

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.

Item Type: Article
Additional Information: Publiziert mit Hilfe des DFG-geförderten Open Access-Publikationsfonds der Carl von Ossietzky Universität Oldenburg.
Uncontrolled Keywords: TUG; IMU; frailty; geriatric assessment; machine learning; wearable sensors; semi-unsupervised; self-assessment; domestic environment; functional decline
Subjects: Technology, medicine, applied sciences > Medicine and health
Divisions: Faculty of Medicine and Health Sciences > Department of Public Health and Medical Education
Date Deposited: 13 Sep 2019 08:26
Last Modified: 20 Sep 2019 09:10
URI: https://oops.uni-oldenburg.de/id/eprint/4179
URN: urn:nbn:de:gbv:715-oops-42603
DOI: doi:10.3390/s18103310
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