CREATING VIRTUAL FORCE PLATFORMS FOR CUTTING MANEUVERS FROM KINEMATIC DATA BASED ON LSTM NEURAL NETWORKS

Authors

Dominik Fohrmann, Institute of Biomechanics and Orthopaedics, German Sport University Cologne, Cologne, GermanyFollow
Marion Mundt, Institute of General Mechanics, RWTH Aachen University, Aachen, GermanyFollow
Sina David, Institute of Biomechanics and Orthopaedics, German Sport University Cologne, Cologne, GermanyFollow
Arnd Koeppe, Institute of General Mechanics, RWTH Aachen University, Aachen, GermanyFollow
Bernd Markert, Institute of General Mechanics, RWTH Aachen University, Aachen, GermanyFollow
Wolfgang Potthast, Institute of Biomechanics and Orthopaedics, German Sport University Cologne, Cologne, GermanyFollow

Category

Modelling / Simulation

Document Type

Paper

Abstract

The precise measurement of ground reaction forces and moments (GRF/M) usually requires stationary equipment and is, therefore, only partly feasible for field measurements. In this work we propose a method to derive GRF/M time series from motion capture marker trajectories for cutting maneuvers (CM) using a long short-term memory (LSTM) neural network. We used a dataset containing 637 CM motion files from 70 participants and trained two-layer LSTM neural networks to predict the GRF/M signals of two force platforms. A five-fold cross-validation resulted in correlation coefficients ranging from 0.870 to 0.977 and normalized root mean square errors from 3.51 to 9.99% between predicted and measured GRF/M. In future, this method can be used not only to simplify lab measurements but also to allow for determining biomechanical parameters during real-world situations.

Recommended Citation

Fohrmann, Dominik; Mundt, Marion; David, Sina; Koeppe, Arnd; Markert, Bernd; and Potthast, Wolfgang (2020) "CREATING VIRTUAL FORCE PLATFORMS FOR CUTTING MANEUVERS FROM KINEMATIC DATA BASED ON LSTM NEURAL NETWORKS," ISBS Proceedings Archive: Vol. 38: Iss. 1, Article 109.
Available at: https://commons.nmu.edu/isbs/vol38/iss1/109