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

Smith, LC, Preece, SJ ORCID: https://orcid.org/0000-0002-2434-732X, Mason, D and Bramah, CA ORCID: https://orcid.org/0000-0003-3644-9873 2015, 'A comparison of kinematic algorithms to estimate gait events during overground running' , Gait & Posture, 41 (1) , pp. 39-43.

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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±17.1ms) and contact time (True Error 3.5±18.2ms). 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.

Item Type: Article
Themes: Health and Wellbeing
Schools: Schools > School of Health and Society > Centre for Health Sciences Research
Journal or Publication Title: Gait & Posture
Publisher: Elsevier
Refereed: Yes
ISSN: 0966-6362
Related URLs:
Funders: Non funded research
Depositing User: SJ Preece
Date Deposited: 22 Sep 2014 14:43
Last Modified: 15 Feb 2022 18:39
URI: https://usir.salford.ac.uk/id/eprint/32831

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