Effects of segment masses and cut-off frequencies on the estimation of vertical ground reaction forces in running

Sokratis Komaris*, Eduardo Perez-Valero, Luke Jordan, John Barton, Liam Hennessy, Brendan O'Flynn, Salvatore Tedesco

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review


The purpose of this study is to examine the effect of the body's mass distribution to segments and the filtering of kinematic data on the estimation of vertical ground reaction forces from positional data. A public dataset of raw running biomechanics was used for the purposes of the analysis, containing recordings of twenty-eight competitive or elite athletes running on an instrumented treadmill at three different speeds. A grid-search on half of the trials was employed to seek the values of the parameters that optimise the approximation of biomechanical loads. Two-way ANOVAs were then conducted to examine the significance of the parameterised factors in the modelled waveforms. The reserved recordings were used to validate the predictive accuracy of the model. The cut-off filtering frequencies of the pelvis and thigh markers were correlated to running speed and heel-strike patterns, respectively. Optimal segment masses were in agreement with standardised literature reported values. Root mean square errors for slow running (2.5 m/s) were on average equal to 0.1 (body weight normalized). Errors increased with running speeds to 0.13 and 0.18 for 3.5 m/s and 4.5 m/s, respectively. This study accurately estimated vertical ground reaction forces for slow-paced running by only considering the kinematics of the pelvis and thighs. Future studies should consider configuring the filtering of kinematic inputs based on the location of markers and type of running.

Original languageEnglish
Article number109552
Number of pages7
JournalJournal of Biomechanics
Early online date9 Dec 2019
Publication statusPublished - 23 Jan 2020

Bibliographical note

© 2019 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Funding Information:
This work was supported in part by the Enterprise Ireland and Setanta College Ltd., under Agreement IP 2017 0606, and in part by the European Regional Development Fund (ERDF) through the Ireland’s European Structural and Investment Funds Programmes 2014-2020. Aspects of this publication have emanated from research conducted with the financial support of Science Foundation Ireland under Grant number 12/RC/2289-P2 INSIGHT which is co-funded under the European Regional Development Fund.


  • Accelerometry
  • Biomechanical modelling
  • Impact forces
  • Kinetics
  • Running performance
  • Wearable sensors


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