Ground reaction forces predicted by using artificial neural network during asymmetric movements - Choi, Ahnryul; Lee, Jae-Moon; Mun, Joung Hwan (International journal of precision engineering and manufacturing, 2013)
During the motion analysis, joint moments are calculated by the inverse dynamics method. The ground reaction forces are representative input data for an application of the inverse dynamics technique, and extracted from an installed force platform. However, when the vast majority of human body movement, continuous and wide spaced, is considered, usage of the fixed force platform on analyzing exercises has many limitations. Therefore, the purpose of this study is to suggest a method to predict the ground reaction forces which occur in complex planes during asymmetric movements. Thirteen healthy male subjects performed static posture, gait and asymmetric movements. The experimental equipment included six infrared cameras and two force platforms, and the artificial neural network was used to solve indeterminate problems occurring in a double support phase. The final 13 input variables from a variety of kinematic and kinetic data for the model were carried out by the Self Organizing Map-Genetic Algorithm General Regression Neural Network, and calculated for one side of ground reaction force from the ‘13-26-1’ of a multi-layer neural network and other one from a dynamic equation. As a result, the correlation coefficients between predicted and measured values were 0.88, 0.47, 0.99 and 0.19 N/kg, 0.25 N/kg, 0.61 N/kg of RMSE at the mediolateral, anterior-posterior, vertical axis, respectively. Especially, the highest prediction rate was shown at the vertical direction ground reaction force which has the biggest variations in motion and a small error at the anterior-posterior and lateral-medial axis compared with the measured value. The results of this study show that ground reaction forces can predict general human movements without force platforms, and we expect them to be used as basic information to interpret inverse dynamics.