Slijepcevic, D.
, Zeppelzauer, M.
, Schwab, C.
, Raberger, A. -M.
, Dumphart, B.
, Baca, A.
, Breiteneder, C.
, Horsak, B.
4050063
Towards an optimal combination of input signals and derived representations for gait classification based on ground reaction force measurements
The application of machine learning in clinical gait analysis has gained popularity in recent years, e.g. for automatic identification of gait patterns [1]. The main purpose of these methods is to support clinicians during medical decision-making processes. In practice, classification performance is strongly influenced by various factors, such as the employed input signals and the selection of derived signal representations. There is currently no best-practice recommendation available on this topic.
© Copyright 2010 Gait and Posture. Elsevier. All rights reserved.
| Subjects: | |
|---|---|
| Notations: | biological and medical sciences |
| Published in: | Gait and Posture |
| Language: | English |
| Published: |
2010
|
| Online Access: | https://doi.org/10.1016/j.gaitpost.2018.06.155 |
| Volume: | 65 |
| Issue: | S 1 |
| Pages: | 249-250 |
| Document types: | article |
| Level: | advanced |