Due to its capabilities in analysing injury risk, the ability to analyse an athlete's ground reaction force and joint moments is of high interest in sports biomechanics. However, using force plates for the kinetic measurements influences the athlete's performance. Therefore, this study aims to use a feed-forward neural network to predict hip, knee and ankle joint moments as well as the ground reaction force from kinematic data during the execution and depart contact of a maximum effort 90° cutting manoeuvre. A total number of 525 cutting manoeuvres performed by 55 athletes were used to train and test neural networks. Either marker trajectories or joint angles were used as input data. The correlation coefficient between the measured and predicted data indicated strong correlations. By using joint angles as the input parameters, slightly but not significantly higher accuracy was found in joint moments predictions. The prediction of the ground reaction force showed significantly higher accuracy when using marker trajectories. Hence, the proposed feed-forward neural network method can be used to predict motion kinetics during a fast change of direction. This may allow for the simplification of cutting manoeuvres experimental set-ups for and through the use of inertial sensors. Graphical abstract The left part of the graphical abstract displays the angle progression of the hip, knee and ankle joint as an example of the kinematic input data and is supported by a stick figure of the motion task, a 90° cutting manoeuvre. This data is used to train a feed-forward neural network, which is displayed in the middle. The neural network's output is displayed on the right. As an example of the kinetic data, the joint moments of hip, knee and ankle joint are displayed and supported by a stick figure.
Keywords: Athletic performance; Change of direction; Kinetics; Machine learning; Neural networks (computers).