Literature DB >> 29482463

Validation of the ambient TUG chair with light barriers and force sensors in a clinical trial.

Sebastian Fudickar1, Jörn Kiselev2, Thomas Frenken3, Sandra Wegel2, Slavica Dimitrowska2, Elisabeth Steinhagen-Thiessen2, Andreas Hein1,3.   

Abstract

To initiate appropriate interventions and avoid physical decline, comprehensive measurements are needed to detect functional changes in elderly people at the earliest possible stage. The established Timed Up&Go (TUG) test takes little time and, due to its standardized and easy procedure, can be conducted by elderly people in their own homes without clinical guidance. Therefore, cheap light barriers (LBs) and force sensors (FSs) are well suited ambient sensors that could easily be attached to existing (arm)chairs to measure and report TUG times in order to identify functional decline. We validated the sensitivity of these sensors in a clinical trial with 100 elderlies aged 58-92 years with a mean of 74 (±6.78) years by comparing the sensor-based results with standard TUG measurements using a stopwatch. We further evaluated the accuracy enhancement when calibrating the algorithm via a mixed linear model. With calibration, the LBs achieved a root mean square error (RMSE) of 0.83 s, compared to 1.90 s without, and the FSs achieved 0.90 s compared to 2.12 s without. The suitability of measuring accurate TUG times with each of the ambient sensors and of measuring TUG regularly in the homes of elderly people could be confirmed.

Entities:  

Keywords:  assessment; falls; mobility; older adults

Mesh:

Year:  2018        PMID: 29482463     DOI: 10.1080/10400435.2018.1446195

Source DB:  PubMed          Journal:  Assist Technol        ISSN: 1040-0435


  7 in total

1.  A Deep Learning Approach for TUG and SPPB Score Prediction of (Pre-) Frail Older Adults on Real-Life IMU Data.

Authors:  Björn Friedrich; Sandra Lau; Lena Elgert; Jürgen M Bauer; Andreas Hein
Journal:  Healthcare (Basel)       Date:  2021-02-02

2.  Scanning Laser Rangefinders for the Unobtrusive Monitoring of Gait Parameters in Unsupervised Settings.

Authors:  Sebastian Fudickar; Christian Stolle; Nils Volkening; Andreas Hein
Journal:  Sensors (Basel)       Date:  2018-10-12       Impact factor: 3.576

3.  Measurement System for Unsupervised Standardized Assessment of Timed "Up & Go" and Five Times Sit to Stand Test in the Community-A Validity Study.

Authors:  Sebastian Fudickar; Sandra Hellmers; Sandra Lau; Rebecca Diekmann; Jürgen M Bauer; Andreas Hein
Journal:  Sensors (Basel)       Date:  2020-05-15       Impact factor: 3.576

4.  Measurement System for Unsupervised Standardized Assessments of Timed Up and Go Test and 5 Times Chair Rise Test in Community Settings-A Usability Study.

Authors:  Sebastian Fudickar; Alexander Pauls; Sandra Lau; Sandra Hellmers; Konstantin Gebel; Rebecca Diekmann; Jürgen M Bauer; Andreas Hein; Frauke Koppelin
Journal:  Sensors (Basel)       Date:  2022-01-19       Impact factor: 3.576

5.  App-Based Evaluation of Older People's Fall Risk Using the mHealth App Lindera Mobility Analysis: Exploratory Study.

Authors:  Nicole Strutz; Hanna Brodowski; Joern Kiselev; Anika Heimann-Steinert; Ursula Müller-Werdan
Journal:  JMIR Aging       Date:  2022-08-16

6.  Towards an Automated Unsupervised Mobility Assessment for Older People Based on Inertial TUG Measurements.

Authors:  Sandra Hellmers; Babak Izadpanah; Lena Dasenbrock; Rebecca Diekmann; Jürgen M Bauer; Andreas Hein; Sebastian Fudickar
Journal:  Sensors (Basel)       Date:  2018-10-02       Impact factor: 3.576

7.  Using Sensor Graphs for Monitoring the Effect on the Performance of the OTAGO Exercise Program in Older Adults.

Authors:  Björn Friedrich; Carolin Lübbe; Enno-Edzard Steen; Jürgen Martin Bauer; Andreas Hein
Journal:  Sensors (Basel)       Date:  2022-01-10       Impact factor: 3.576

  7 in total

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