Estimation of Three-Dimensional Ground Reaction Forces During Walking and Turning Using Insole Pressure Sensors Based on Gait Pattern Recognition

Three-dimensional ground reaction forces (3D GRFs) in various gait patterns (e.g., walking and turning) provide essential information for clinical assessment. Previous scholars proposed to estimate GRFs from insoles with few pressure sensors using machine learning techniques. However, estimating GRFs during turning using a model learned from walking proved difficult because relationships between plantar pressure and GRFs vary according to the gait pattern. In this study, GRFs were estimated based on gait pattern recognition. GRF estimation models were learned from various gait patterns in advance. These models represented relationships between the insole measurements and GRFs using a Gaussian process regression (GPR). The GRFs were estimated from the insole measurements using a maximum likelihood (ML) model recognized as the current gait pattern. The ML model had the largest time frames, in which its standard deviation (SD) of the probabilistically estimated vertical GRF was the smallest among all the models (i.e., its training data were closest to the inputs), in the initial and terminal stance phases (subphases). In addition, the time frames of the contralateral leg were considered to enhance the recognition accuracy based on interlimb coordination. Experiment results showed that the proposed system, which used a common model learned from all the gait patterns for the vertical GRF and the ML model in the subphases of both legs for the horizontal GRFs, estimated 3D GRFs during walking and turning with different curvatures and directions with higher accuracy. The system can be widely applied for clinical walking tests and monitoring in daily life.