Towards an optimal combination of input signals and derived representations for gait classification based on ground reaction force measurements - Slijepcevic, D.; Zeppelzauer, M.; Schwab, C.; Raberger, A. -M.; Dumphart, B.; Baca, A.; Breiteneder, C.; Horsak, B. (Gait and Posture, 2010)
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.