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

Research output: Contribution to journalArticle

Authors

  • Mark Pogson
  • Jasper Verheul
  • Mark A. Robinson
  • Jos Vanrenterghem
  • Paulo Lisboa

Colleges, School and Institutes

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 r 2 coefficients with test data averaged around 0.9 for each impact, comparing favourably with alternative approaches which require additional sensors. For the impact peak, r 2 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.

Details

Original languageEnglish
Pages (from-to)82-89
Number of pages8
JournalMedical Engineering & Physics
Volume78
Early online date27 Feb 2020
Publication statusPublished - Apr 2020

Keywords

  • Biomechanical load, Ground reaction force, Multilayer perceptron, Trunk accelerometry

ASJC Scopus subject areas