PREDICTING 3D GROUND REACTION FORCE FROM 2D VIDEO VIA NEURAL NETWORKS IN SIDESTEPPING TASKS

Authors

Corey Morris, University of Western Australia, PerthFollow
Marion Mundt, University of Western Australia, PerthFollow
Molly Goldacre, University of Western Australia, PerthFollow
Jason Weber, University of Western Australia, PerthFollow
Ajmal Mian, University of Western Australia, PerthFollow
Jacqueline Alderson, University of Western Australia, PerthFollow

Category

Injury

Document Type

Paper

Abstract

Sports science practitioners often measure ground reaction forces (GRFs) to assess performance, rehabilitation and injury risk. However, recording of GRFs during dynamic tasks has historically been limited to lab settings. This work aims to use neural networks (NN) to predict three-dimensional (3D) GRF via pose estimation keypoints as inputs, determined from 2D video data. Two different NN were trained on a dataset containing 1474 samples from 14 participants and their prediction accuracy compared with ground truth force data. Results for both NN showed correlation coefficients ranging from 0.936 to 0.954 and normalised root mean square errors from 11.05% to 13.11% for anterior-posterior and vertical GRFs, with poorer results found in the medio-lateral direction. This study demonstrates the feasibility and utility of predicting GRFs from 2D video footage.

Recommended Citation

Morris, Corey; Mundt, Marion; Goldacre, Molly; Weber, Jason; Mian, Ajmal; and Alderson, Jacqueline (2021) "PREDICTING 3D GROUND REACTION FORCE FROM 2D VIDEO VIA NEURAL NETWORKS IN SIDESTEPPING TASKS," ISBS Proceedings Archive: Vol. 39: Iss. 1, Article 77.
Available at: https://commons.nmu.edu/isbs/vol39/iss1/77