Research Article
Novel Approach to Unsupervised Mobility Assessment Tests: Field Trial For aTUG
@INPROCEEDINGS{10.4108/icst.pervasivehealth.2012.248614, author={Thomas Frenken and Myriam Lipprandt and Melina Brell and Mehmet G\o{}vercin and Sandra Wegel and Elisabeth Steinhagen-Thiessen and Andreas Hein}, title={Novel Approach to Unsupervised Mobility Assessment Tests: Field Trial For aTUG}, proceedings={6th International Conference on Pervasive Computing Technologies for Healthcare}, publisher={IEEE}, proceedings_a={PERVASIVEHEALTH}, year={2012}, month={7}, keywords={atug timed up and go (tug) mobility assessment domestic assessment laser range scanner lidar force sensors sensor fusion}, doi={10.4108/icst.pervasivehealth.2012.248614} }- Thomas Frenken
Myriam Lipprandt
Melina Brell
Mehmet Gövercin
Sandra Wegel
Elisabeth Steinhagen-Thiessen
Andreas Hein
Year: 2012
Novel Approach to Unsupervised Mobility Assessment Tests: Field Trial For aTUG
PERVASIVEHEALTH
ICST
DOI: 10.4108/icst.pervasivehealth.2012.248614
Abstract
A novel approach to performing unsupervised mobility assessment tests in domestic environments is presented. As a part of the aTUG concept the approach is based on the idea to segment assessment tests into components made up of recurring movement patterns which are measured independently by use of ambient sensor technologies. Quality criteria are defined which compute a score of eligibility for usage of sensor data to assess a certain test component. Valid component measurements are recombined to complete assessment tests according to a technical assessment test description defining the flow of segments and their constraints. An experiment has been conducted within a field trial with five elderly people aged 64-84 years over five weeks. The flats of all people were equipped with home automation (HA) sensors. A laser range scanner (LRS) was placed in one flat. Results from the fully-equipped flat show that the presented quality criteria are suitable to select LRS measurements according to their eligibility to assess a certain component. HA sensors and the LRS were used to compute a self-selected gait velocity of 0.71m/s unsupervised at home. TUG using the aTUG apparatus and a stopwatch was used as clinical reference data yielding a mean gait velocity of 1.18m/s. For the described setting a difference of 0.47m/s between capacity and performance in gait velocity was found.