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The feasibility of predicting ground reaction forces during running from a trunk accelerometry driven mass-spring-damper model

Nedergaard, NJ, Verheul, JP, Drust, B, Etchells, T, Lisboa, P, Robinson, MA and Vanrenterghem, J (2018) The feasibility of predicting ground reaction forces during running from a trunk accelerometry driven mass-spring-damper model. PeerJ. ISSN 2167-8359

2018 Nedergaard PeerJ Predicting GRF from Trunk Acc.pdf - Published Version
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Monitoring the external ground reaction forces (GRF) acting on the human body during running could help to understand how external loads influence tissue adaptation over time. Although mass-spring-damper (MSD) models have the potential to simulate the complex multi-segmental mechanics of the human body and predict GRF, these models currently require input from measured GRF limiting their application in field settings. Based on the hypothesis that the acceleration of the MSD-model’s upper mass primarily represents the acceleration of the trunk segment, this paper explored the feasibility of using measured trunk accelerometry to estimate the MSD-model parameters required to predict resultant GRF during running.

Twenty male athletes ran at approach speeds between 2–5 m s−1. Resultant trunk accelerometry was used as a surrogate of the MSD-model upper mass acceleration to estimate the MSD-model parameters (ACCparam) required to predict resultant GRF. A purpose-built gradient descent optimisation routine was used where the MSD-model’s upper mass acceleration was fitted to the measured trunk accelerometer signal. Root mean squared errors (RMSE) were calculated to evaluate the accuracy of the trunk accelerometry fitting and GRF predictions. In addition, MSD-model parameters were estimated from fitting measured resultant GRF (GRFparam), to explore the difference between ACCparam and GRFparam.

Despite a good match between the measured trunk accelerometry and the MSD-model’s upper mass acceleration (median RMSE between 0.16 and 0.22 g), poor GRF predictions (median RMSE between 6.68 and 12.77 N kg−1) were observed. In contrast, the MSD-model was able to replicate the measured GRF with high accuracy (median RMSE between 0.45 and 0.59 N kg−1) across running speeds from GRFparam. The ACCparam from measured trunk accelerometry under- or overestimated the GRFparam obtained from measured GRF, and generally demonstrated larger within parameter variations.

Despite the potential of obtaining a close fit between the MSD-model’s upper mass acceleration and the measured trunk accelerometry, the ACCparam estimated from this process were inadequate to predict resultant GRF waveforms during slow to moderate speed running. We therefore conclude that trunk-mounted accelerometry alone is inappropriate as input for the MSD-model to predict meaningful GRF waveforms. Further investigations are needed to continue to explore the feasibility of using body-worn micro sensor technology to drive simple human body models that would allow practitioners and researchers to estimate and monitor GRF waveforms in field settings.

Item Type: Article
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions: Applied Mathematics
Sport & Exercise Sciences
Publisher: PeerJ Inc.
Date Deposited: 20 Dec 2018 16:14
Last Modified: 20 Dec 2018 16:16
DOI or Identification number: 10.7717/peerj.6105
URI: http://researchonline.ljmu.ac.uk/id/eprint/9861

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