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Can an inertial measurement unit, combined with machine learning, accurately measure ground reaction forces in cricket fast bowling?

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posted on 2025-04-09, 11:40 authored by Joseph W McGrath, Jonathon Neville, Tom Stewart, Matt Lamb, Peter Alway, Mark KingMark King, John Cronin
This study examined whether an inertial measurement unit (IMU) could measure ground reaction force (GRF) during a cricket fast bowling delivery. Eighteen male fast bowlers had IMUs attached to their upper back and bowling wrist. Each participant bowled 36 deliveries, split into three different intensity zones: low = 70% of maximum perceived bowling effort, medium = 85%, and high = 100%. A force plate was embedded into the bowling crease to measure the ground truth GRF. Three machine learning models were used to estimate GRF from the IMU data. The best results from all models showed a mean absolute percentage error of 22.1% body weights (BW) for vertical and horizontal peak force, 24.1% for vertical impulse, 32.6% and 33.6% for vertical and horizontal loading rates, respectively. The linear support vector machine model had the most consistent results. Although results were similar to other papers that have estimated GRF, the error would likely prevent its use in individual monitoring. However, due to the large differences in raw GRFs between participants, researchers may be able to help identify links among GRF, injury, and performance by categorising values into levels (i.e., low and high).

History

School

  • Sport, Exercise and Health Sciences

Published in

Sports Biomechanics

Publisher

Informa UK Limited, trading as Taylor & Francis Group

Version

  • VoR (Version of Record)

Rights holder

©The Author(s)

Publisher statement

This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Acceptance date

2023-01-17

Publication date

2023-11-09

Copyright date

2023

ISSN

1476-3141

eISSN

1752-6116

Language

  • en

Depositor

Prof Mark King. Deposit date: 30 October 2024

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