Literature DB >> 15531173

Assessment and validation of a simple automated method for the detection of gait events and intervals.

Salim Ghoussayni1, Christopher Stevens, Sally Durham, David Ewins.   

Abstract

A simple and rapid automatic method for detection of gait events at the foot could speed up and possibly increase the repeatability of gait analysis and evaluations of treatments for pathological gaits. The aim of this study was to compare and validate a kinematic-based algorithm used in the detection of four gait events, heel contact, heel rise, toe contact and toe off. Force platform data is often used to obtain start and end of contact phases, but not usually heel rise and toe contact events. For this purpose synchronised kinematic, kinetic and video data were captured from 12 healthy adult subjects walking both barefoot and shod at slow and normal self-selected speeds. The data were used to determine the gait events using three methods: force, visual inspection and algorithm methods. Ninety percent of all timings given by the algorithm were within one frame (16.7 ms) when compared to visual inspection. There were no statistically significant differences between the visual and algorithm timings. For both heel and toe contact the differences between the three methods were within 1.5 frames, whereas for heel rise and toe off the differences between the force on one side and the visual and algorithm on the other were higher and more varied (up to 175 ms). In addition, the algorithm method provided the duration of three intervals, heel contact to toe contact, toe contact to heel rise and heel rise to toe off, which are not readily available from force platform data. The ability to automatically and reliably detect the timings of these four gait events and three intervals using kinematic data alone is an asset to clinical gait analysis.

Mesh:

Year:  2004        PMID: 15531173     DOI: 10.1016/j.gaitpost.2003.10.001

Source DB:  PubMed          Journal:  Gait Posture        ISSN: 0966-6362            Impact factor:   2.840


  37 in total

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Authors:  Ziba Gandomkar; Fariba Bahrami
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2.  Two simple methods for determining gait events during treadmill and overground walking using kinematic data.

Authors:  J A Zeni; J G Richards; J S Higginson
Journal:  Gait Posture       Date:  2007-08-27       Impact factor: 2.840

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4.  Lower extremity biomechanical relationships with different speeds in traditional, minimalist, and barefoot footwear.

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5.  Estimation of Temporal Gait Events from a Single Accelerometer Through the Scale-Space Filtering Idea.

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Journal:  J Med Syst       Date:  2016-10-06       Impact factor: 4.460

6.  Adaptive control of center of mass (global) motion and its joint (local) origin in gait.

Authors:  Feng Yang; Yi-Chung Pai
Journal:  J Biomech       Date:  2014-06-11       Impact factor: 2.712

7.  Validation of simplified centre of mass models during gait in individuals with chronic stroke.

Authors:  Andrew H Huntley; Alison Schinkel-Ivy; Anthony Aqui; Avril Mansfield
Journal:  Clin Biomech (Bristol, Avon)       Date:  2017-07-31       Impact factor: 2.063

8.  Identification of gait events in children with spastic cerebral palsy: comparison between the force plate and algorithms.

Authors:  Rejane Vale Gonçalves; Sérgio Teixeira Fonseca; Priscila Albuquerque Araújo; Vanessa Lara Araújo; Tais Martins Barboza; Gabriela Andrade Martins; Marisa Cotta Mancini
Journal:  Braz J Phys Ther       Date:  2019-06-12       Impact factor: 3.377

9.  Two types of slip-induced falls among community dwelling older adults.

Authors:  Feng Yang; Debbie Espy; Tanvi Bhatt; Yi-Chung Pai
Journal:  J Biomech       Date:  2012-02-15       Impact factor: 2.712

10.  Differentiating fall-prone and healthy adults using local dynamic stability.

Authors:  Thurmon E Lockhart; Jian Liu
Journal:  Ergonomics       Date:  2008-12       Impact factor: 2.778

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