Literature DB >> 25212739

A comparison of kinematic algorithms to estimate gait events during overground running.

Laura Smith1, Stephen Preece2, Duncan Mason2, Christopher Bramah2.   

Abstract

The gait cycle is frequently divided into two distinct phases, stance and swing, which can be accurately determined from ground reaction force data. In the absence of such data, kinematic algorithms can be used to estimate footstrike and toe-off. The performance of previously published algorithms is not consistent between studies. Furthermore, previous algorithms have not been tested at higher running speeds nor used to estimate ground contact times. Therefore the purpose of this study was to both develop a new, custom-designed, event detection algorithm and compare its performance with four previously tested algorithms at higher running speeds. Kinematic and force data were collected on twenty runners during overground running at 5.6m/s. The five algorithms were then implemented and estimated times for footstrike, toe-off and contact time were compared to ground reaction force data. There were large differences in the performance of each algorithm. The custom-designed algorithm provided the most accurate estimation of footstrike (True Error 1.2 ± 17.1 ms) and contact time (True Error 3.5 ± 18.2 ms). Compared to the other tested algorithms, the custom-designed algorithm provided an accurate estimation of footstrike and toe-off across different footstrike patterns. The custom-designed algorithm provides a simple but effective method to accurately estimate footstrike, toe-off and contact time from kinematic data.
Copyright © 2014 Elsevier B.V. All rights reserved.

Keywords:  Event detection; Gait events; Kinematic algorithm; Phase determination; Running

Mesh:

Year:  2014        PMID: 25212739     DOI: 10.1016/j.gaitpost.2014.08.009

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


  5 in total

1.  Quantification of Triple Single-Leg Hop Test Temporospatial Parameters: A Validated Method using Body-Worn Sensors for Functional Evaluation after Knee Injury.

Authors:  Niloufar Ahmadian; Milad Nazarahari; Jackie L Whittaker; Hossein Rouhani
Journal:  Sensors (Basel)       Date:  2020-06-19       Impact factor: 3.576

2.  Sex-related differences in coordination and variability among foot joints during running.

Authors:  Tomoya Takabayashi; Mutsuaki Edama; Takuma Inai; Masayoshi Kubo
Journal:  J Foot Ankle Res       Date:  2018-09-17       Impact factor: 2.303

3.  A Single Sacral-Mounted Inertial Measurement Unit to Estimate Peak Vertical Ground Reaction Force, Contact Time, and Flight Time in Running.

Authors:  Aurélien Patoz; Thibault Lussiana; Bastiaan Breine; Cyrille Gindre; Davide Malatesta
Journal:  Sensors (Basel)       Date:  2022-01-20       Impact factor: 3.576

4.  Duty factor and foot-strike pattern do not represent similar running pattern at the individual level.

Authors:  Aurélien Patoz; Thibault Lussiana; Bastiaan Breine; Cyrille Gindre; Davide Malatesta
Journal:  Sci Rep       Date:  2022-07-29       Impact factor: 4.996

5.  Evaluating the Accuracy of Virtual Reality Trackers for Computing Spatiotemporal Gait Parameters.

Authors:  Michelangelo Guaitolini; Fitsum E Petros; Antonio Prado; Angelo M Sabatini; Sunil K Agrawal
Journal:  Sensors (Basel)       Date:  2021-05-11       Impact factor: 3.576

  5 in total

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