INSOLE-BASED ESTIMATION OF COMPLETE GROUND REACTION FORCE WITH GAUSSIAN KERNEL REGRESSION AND DATA EXPANSION

Abstract

A method for estimating ground reaction force (GRF) with plantar pressure was proposed in this paper. The estimation model was constructed to approximate the nonlinear relationships between GRF and the plantar pressure according to the linear combinations of Gaussian kernel functions. Partial least squares regression (PLSR) was adopted to obtain model parameters and eliminate multicollinearity among the pressure components. The general model and subject-specific models were constructed for 12 male and 4 female subjects. Moreover, a data expansion method was introduced for the establishment of subject-specific model, which is implemented by searching and adopting the data with consistent statistical characteristics in a pre-established database. That approach is particularly meaningful for the group whose walking ability is limited or clinic where the force platform is not available. The NRMSEs (%) for general model were 5.27–7.85% (GRF_V), 7.35–8.53% (GRF_ML), and 8.82–10.54% (GRF_AP). The maximum NRMSEs (%) for subject-specific models were 5.02% (GRF_V), 9.91% (GRF_ML), and 10.23% (GRF_AP). Results showed that both general and subject-specific models achieved higher accuracy than existing methods such as linear regression and neural network methods.