Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning

Johnson, WR, Mian, A, Robinson, MA, Verheul, J, Lloyd, DG and Alderson, JA (2020) Multidimensional ground reaction forces and moments from wearable sensor accelerations via deep learning. IEEE Transactions on Biomedical Engineering, 68 (1). 289 - 297. ISSN 0018-9294

[img]
Preview
Text
Multidimensional Ground Reaction Forces and Moments From Wearable Sensor Accelerations via Deep Learning.pdf - Accepted Version

Download (1MB) | Preview

Abstract

Objective: Monitoring athlete internal workload exposure, including prevention of catastrophic non-contact knee injuries, relies on the existence of a custom early-warning detection system. This system must be able to estimate accurate, reliable, and valid musculoskeletal joint loads, for sporting maneuvers in near real-time and during match play. However, current methods are constrained to laboratory instrumentation, are labor and cost intensive, and require highly trained specialist knowledge, thereby limiting their ecological validity and volume deployment. Methods: Here we show that kinematic data obtained from wearable sensor accelerometers, in lieu of embedded force platforms, can leverage recent supervised learning techniques to predict in-game near real-time multidimensional ground reaction forces and moments (GRF/M). Competing convolutional neural network (CNN) deep learning models were trained using laboratory-derived stance phase GRF/M data and simulated sensor accelerations for running and sidestepping maneuvers derived from nearly half a million legacy motion trials. Then, predictions were made from each model driven by five sensor accelerations recorded during independent inter-laboratory data capture sessions. Results: Despite adversarial conditions, the proposed deep learning workbench achieved correlations to ground truth, by GRF component, of vertical 0.9663, anterior 0.9579 (both running), and lateral 0.8737 (sidestepping). Conclusion: The lessons learned from this study will facilitate the use of wearable sensors in conjunction with deep learning to accurately estimate near real-time on-field GRF/M. Significance: Coaching, medical, and allied health staff can use this technology to monitor a range of joint loading indicators during game play, with the ultimate aim to minimize the occurrence of non-contact injuries in elite and community-level sports.

Item Type: Article
Additional Information: © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Uncontrolled Keywords: cs.CV; cs.CV
Subjects: R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions: Sport & Exercise Sciences
Publisher: IEEE
Related URLs:
Date Deposited: 15 Jul 2021 12:56
Last Modified: 15 Jul 2021 13:00
DOI or Identification number: 10.1109/TBME.2020.3006158
URI: https://researchonline.ljmu.ac.uk/id/eprint/11609

Actions (login required)

View Item View Item