Literature DB >> 31113574

Gait events during turning can be detected using kinematic features originally proposed for the analysis of straight-line walking.

Baptiste Ulrich1, Alejandro N Santos2, Brigitte M Jolles3, David H Benninger2, Julien Favre4.   

Abstract

There is a growing interest for turning biomechanics notably because it is a more challenging task than straight-line walking during which some gait impairments are increased. Detecting heel-strike (HS) and toe-off (TO) events using the trajectory of markers attached to the feet is common in straight-line gait analysis and could reveal very useful to evaluate turning maneuvers. Yet, a comprehensive evaluation is missing, making difficult the selection of features for temporal analysis of turning. This study aimed to compare features of foot marker trajectories to detect HS and TO. Twenty healthy participants, 10 young (5 males, 23 ± 1 years old, 21.3 ± 2.2 kg/m2) and 10 elderly (4 males, 72 ± 5 years old, 26.4 ± 6.4 kg/m2), performed quarter, half, and full turns as well as straight-line walking in a gait lab. Fourteen features, adapted from straight-line walking literature, were used to detect HS and TO based on marker trajectories. Force plate measures served as reference. One HS and one TO feature were found particularly suitable. Overall, they detected more than 99% of the 1788 events recorded, with accuracies and precisions of -3.9 ms and 9.0 ms for HS and -7.8 ms and 10.7 ms for TO, respectively. Differences in accuracy and precision were observed among walking conditions and groups, but remained small, generally below 4.0 ms. In conclusion, this study identified kinematic features that can be used to analyze both turning and straight-line walking. Further assessment could be necessary with pathologies inducing severe degradation of gait patterns.
Copyright © 2019 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Gait analysis; Motion capture; Stance phase; Temporal parameters and turning

Mesh:

Year:  2019        PMID: 31113574     DOI: 10.1016/j.jbiomech.2019.05.006

Source DB:  PubMed          Journal:  J Biomech        ISSN: 0021-9290            Impact factor:   2.712


  1 in total

1.  An Algorithm for Accurate Marker-Based Gait Event Detection in Healthy and Pathological Populations During Complex Motor Tasks.

Authors:  Tecla Bonci; Francesca Salis; Kirsty Scott; Lisa Alcock; Clemens Becker; Stefano Bertuletti; Ellen Buckley; Marco Caruso; Andrea Cereatti; Silvia Del Din; Eran Gazit; Clint Hansen; Jeffrey M Hausdorff; Walter Maetzler; Luca Palmerini; Lynn Rochester; Lars Schwickert; Basil Sharrack; Ioannis Vogiatzis; Claudia Mazzà
Journal:  Front Bioeng Biotechnol       Date:  2022-06-02
  1 in total

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