Prediction of Ground Reaction Forces and Moments via Supervised Learning Is Independent of Participant Sex, Height and Mass

Accurate multidimensional ground reaction forces and moments (GRF/Ms) can be predicted from marker-based motion capture using Partial Least Squares (PLS) supervised learning. In this study, the correlations between known and predicted GRF/Ms are compared depending on whether the PLS model is trained using the discrete inputs of sex, height and mass. All three variables were found to be accounted for in the marker trajectory data, which serves to simplify data capture requirements and importantly, indicates that prediction of GRF/Ms can be achieved without pre-existing knowledge of such participant specific inputs. This multidisciplinary research approach significantly advances machine representation of real-world physical attributes with direct application to sports biomechanics.