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A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities

Pogson, MA, Verheul, J, Robinson, MA, Vanrenterghem, J and Lisboa, P A neural network method to predict task- and step-specific ground reaction force magnitudes from trunk accelerations during running activities. Medical Engineering and Physics. ISSN 1350-4533 (Accepted)

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Abstract

Prediction of ground reaction force (GRF) magnitudes during running-based sports has several important applications, including optimal load prescription and injury prevention in athletes. Existing methods typically require information from multiple body-worn sensors, limiting their ecological validity, or aim to estimate discrete force parameters, limiting their ability to assess overall biomechanical load. This paper presents a neural network method to predict GRF time series from a single, commonly used, trunk-mounted accelerometer. The presented method uses a principal component analysis and multilayer perceptron (MLP) to obtain predictions. Time-series r2 correlation with test data averaged around 0.9 for each impact, comparing favourably with alternative approaches which require additional sensors. For the impact peak, r2 correlation was 0.74 across activities, comparing favourably with correlation analysis approaches. Several modifications, such as subject-specific training of the MLP, may help to improve results further, but the presented method can accurately predict GRF from trunk accelerometry data without requiring additional information. Results demonstrate the scope of machine learning to exploit common wearable technologies to estimate GRF in sport-specific environments.

Item Type: Article
Uncontrolled Keywords: 02 Physical Sciences, 09 Engineering, 11 Medical and Health Sciences
Subjects: R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions: Applied Mathematics
Sports & Exercise Sciences
Publisher: Elsevier
Date Deposited: 12 Feb 2020 09:52
Last Modified: 12 Feb 2020 10:00
URI: http://researchonline.ljmu.ac.uk/id/eprint/12245

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