@article{ref1,
title="Machine learning based walking aid detection in timed up-and-go test recordings of elderly patients",
journal="Annual International Conference of the IEEE Engineering in Medicine and Biology Society.",
year="2020",
author="Ziegl, Andreas and Hayn, Dieter and Kastner, Peter and Loffler, Kerstin and Weidinger, Lisa and Brix, Bianca and Goswami, Nandu and Schreier, Gunter",
volume="2020",
number="",
pages="808-811",
abstract="Frailty and falls are the main causes of morbidity and disability in elderly people. The Timed Up-and-Go (TUG) test has been proposed as an appropriate method for evaluating elderly individuals' risk of falling. To analyze the TUG's potential for falls prediction, we conducted a clinical study with participants aged ≥ 65 years, living in nursing homes. We harvested 138 TUG recordings with the information, if patients used a walking aid or not and developed a method to predict the use of walking aids using a Random Forest Classifier for ultrasonic based TUG test recordings. We achieved a high accuracy with an Area Under the Curve (AUC) of 96,9% using a 20% leave out evaluation strategy. Automated collection of structured data from TUG recordings - like the use of a walking aid - may help to improve fall risk tools in future.<p /> <p>Language: en</p>",
language="en",
issn="2375-7477",
doi="10.1109/EMBC44109.2020.9176574",
url="http://dx.doi.org/10.1109/EMBC44109.2020.9176574"
}