Name
Title | ||
|---|---|---|
Machine Learning Based Walking Aid Detection In Timed Up-And-Go Test Recordings Of Elderly Patients |
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. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1109/EMBC44109.2020.9176574 | 42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20 |
DocType | Volume | ISSN |
Conference | 2020 | 1557-170X |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Andreas Ziegl | 1 | 0 | 0.68 |
| Dieter Hayn | 2 | 28 | 9.62 |
| Peter Kastner | 3 | 0 | 2.37 |
| Kerstin Loffler | 4 | 0 | 0.34 |
| Lisa Weidinger | 5 | 0 | 0.34 |
| Bianca Brix | 6 | 0 | 0.34 |
| Nandu Goswami | 7 | 0 | 0.34 |
| Günter Schreier | 8 | 56 | 23.73 |