Abstract
The Timed Up and Go (TUG) is a clinical test used widely to measure balance and mobility, e.g. in Parkinson's disease (PD). The test includes a sequence of functional activities, namely: sit-to-stand, 3-meters walk, 180° turn, walk back, another turn and sit on the chair. Meanwhile the stopwatch is used to score the test by measuring the time which the patients with PD need to perform the test. Here, the work presents an instrumented TUG using a wearable inertial sensor unit attached on the lower back of the person. The approach is used to automate the process of assessment compared with the manual evaluation by using visual observation and a stopwatch. The developed algorithm is based on the Dynamic Time Warping (DTW) for multi-dimensional time series and has been applied with the augmented feature for detection and duration assessment of turn state transitions, while a 1-dimensional DTW is used to detect the sit-to-stand and stand-to-sit phases. The feature set is a 3-dimensional vector which consists of the angular velocity, derived angle and features from Linear Discriminant Analysis (LDA). The algorithm was tested on 10 healthy individuals and 20 patients with PD (10 patients with early and late disease phases respectively). The test demonstrates that the developed technique can successfully extract the time information of the sit-to-stand, both turns and stand-to-sit transitions in the TUG test. © 2012 IEEE.
| Originalsprache | Englisch |
|---|---|
| Titel | IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems |
| Seitenumfang | 7 |
| Erscheinungsdatum | 2012 |
| Seiten | 212-218 |
| ISBN (Print) | 9781467325110 |
| DOIs | |
| Publikationsstatus | Veröffentlicht - 2012 |
UN SDGs
Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung
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SDG 3 – Gesundheit und Wohlergehen
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SDG 10 – Weniger Ungleichheiten
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