Medizinische Universität Graz - Research portal
Selected Publication:
SHR Neuro Cancer Cardio Lipid Metab Microb
Ziegl, A; Hayn, D; Kastner, P; Loffler, K; Weidinger, L; Brix, B; Goswami, N; Schreier, G.
Machine Learning Based Walking Aid Detection in Timed Up-and-Go Test Recordings of Elderly Patients.
Annu Int Conf IEEE Eng Med Biol Soc. 2020; 2020: 808-811.
Doi: 10.1109/EMBC44109.2020.9176574
[Oral Communication]
PubMed
FullText
FullText_MUG
- Co-authors Med Uni Graz
- Goswami Nandu
- Steuber Bianca
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- 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.