Prediction of active peak force using a multilayer perceptron

Both kinematic parameters and ground reaction forces (GRFs) are necessary for understanding the biomechanics of running. Kinematic information of a runner is typically measured by a motion capture system whereas GRF during the support phase of running is measured by force platforms. To analyze both kinematics and kinetics of a runner over several subsequent contacts, an instrumented treadmill or alternatively several force platforms installed over a regulated space are available options, but they are highly immovable, expensive, and sometimes even impractical options. Naturally, it would be highly useful to predict GRFs using a motion capture system only and this way reduce costs and complexity of the analysis. In this study, the machine learning model for vertical GRF magnitude prediction based on running motion information of 128 healthy adults is proposed. The predicted outputs of a multilayer perceptron model were compared with the actual force platform measurements. The results were evaluated with Pearson’s correlation coefficient through a tenfold cross validation. The mean standard error of the estimate was 0.107 body weights showing that our method is sufficiently accurate to identify abnormalities in running technique among recreational runners.