Convolutional neural network based ground reaction forces and center of pressure estimation during stair walking using multi-level kinematics

Abstract

Ground reaction forces (GRFs) and center of pressure trajectories (CoPs) are required for a comprehensive biomechanical analysis. They are also important outcome measures in sports sciences or clinical areas. GRFs and CoPs are usually measured by force plates, which are rarely equipped on staircases in laboratories. We present a one-dimensional convolutional neural network for estimating GRFs and CoPs during stair ascent and descent using multi-level kinematics as input. We collected a dataset of 3782 trials from 172 subjects for training and validating this model. The recruited subjects include healthy subjects and individuals with knee osteoarthritis or moderate-to-high risk of cardiovascular diseases. Our model achieves the state-of-the-art estimating performance with nRMSE of 2.755%∼7.633%, Pearson correlation of 0.950∼0.996 on GRFs estimation, and with nRMSE of 5.519%∼14.669%, Pearson correlation of 0.918∼0.991 on CoPs estimation. With our proposed model, GRFs and CoPs during stair walking can be estimated without force-plate-embedded staircases.