Improving the ground reaction force prediction accuracy using one-axis plantar pressure: Expansion of input variable for neural network

In this study, we describe a method to predict 6-axis ground reaction forces based solely on plantar pressure (PP) data obtained from insole type measurement devices free of space limitations. Because only vertical force is calculable from PP data, a wavelet neural network derived from a non-linear mapping function was used to obtain 3-axis ground reaction force in medial-lateral (GRF_ML), anterior-posterior (GRF_AP) and vertical (GRF_V) and 3-axis ground reaction moment in sagittal (GRF_S), frontal (GRF_F) and transverse (GRF_T) data for the remaining axes and planes. As the prediction performance of nonlinear models depends strongly on input variables, in this study, three input variables – accumulated PP with respect to time, center of pressure (COP) pattern, and measurements of the opposite foot, which are calculable only with a PP device – were considered in order to improve prediction performance. To conduct this study, the golf swing motions of 80 subjects were characterized as unilateral movement and GRF patterns as functions of individual characteristics. The prediction model was verified with 5-fold cross-validation utilizing the measured values of two force plates. As a result, prediction model (correlation coefficient, r=0.73–0.97) utilized accumulated PP and PP data of the opposite foot and showed the highest prediction accuracy in left-foot GRF_V, GRM_F, GRM_T and right-foot GRF_AP, GRF_ML, GRM_F, GRM_T. Likewise, another prediction model (r=0.83–0.98) utilized accumulated PP and COP patterns as input and showed the best accuracy in left-foot GRF_AP, GRF_ML, GRM_S and right-foot GRF_V, GRM_S. New methods based on the findings of the present study are expected to help resolve problems such as spatial limitation and limited analyzable motions in existing GRF measurement processes.