Predicting gait events from tibial acceleration in rearfoot running: A structured machine learning approach.

Journal: Gait & posture
Published Date: Nov 10, 2020

Abstract

BACKGROUND: Gait event detection of the initial contact and toe off is essential for running gait analysis, allowing the derivation of parameters such as stance time. Heuristic-based methods exist to estimate these key gait events from tibial accelerometry. However, these methods are tailored to very specific acceleration profiles, which may offer complications when dealing with larger data sets and inherent biological variability.

Authors

  • Pieter Robberechts
    Department of Computer Science, KU Leuven, Celestijnenlaan 200A Box 2402, 3001, Heverlee, Belgium. Electronic address: pieter.robberechts@cs.kuleuven.be.
  • Rud Derie
    Department of Movement and Sports Sciences, Ghent University, Watersportlaan 2, 9000, Gent, Belgium. Electronic address: rud.derie@ugent.be.
  • Pieter Van den Berghe
    Department of Movement and Sports Sciences, Ghent University, B-9000, Ghent, Belgium.
  • Joeri Gerlo
    Department of Movement and Sports Sciences, Ghent University, Watersportlaan 2, 9000, Gent, Belgium.
  • Dirk De Clercq
    Department of Movement and Sports Sciences, Ghent University, B 9000 Ghent, Belgium.
  • Veerle Segers
    a Faculty of Medicine and Health Sciences , Department of Movement and Sports Sciences , Gent , Belgium.
  • Jesse Davis
    Department of Computer Science, KU Leuven, Celestijnenlaan 200A Box 2402, 3001, Heverlee, Belgium.

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