Ferroelectret-Based Insole for Vertical Ground Reaction Force Estimation Using a Convolutional Neural Network

Abstract

Precise and portable ground reaction force (GRF) measurement is critical for advancing biomechanical gait analysis and enabling more effective control of robots and assistive devices. This study investigates vertical GRF estimation during walking using a soft, lightweight, and cost-effective 3D-printed ferroelectret insole. The insole design incorporates four monolithically 3D-printed piezoelectric sensors positioned under key foot contact areas, which generate nonlinear voltage in response to applied forces. A 1-D convolutional neural network (CNN), featuring two convolutional and two fully connected layers, was trained to predict vertical GRF across five different walking speeds (50–150% of normal walking speed). The CNN was validated using K-fold cross-validation, enhancing model generalization. Results showed an average root-mean-squared error of 9.24% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$R^{2}$</tex-math></inline-formula> values exceeding 0.99 across different speeds, demonstrating the potential of 3D-printed ferroelectret sensors for portable GRF measurement in gait analysis and robotics applications.