High-Resolution Plantar Pressure Insole System for Enhanced Lower Body Biomechanical Analysis

Gait analysis is a crucial method for evaluating and monitoring an individual's health. A critical aspect of this analysis is understanding how forces are distributed across the foot while walking. Existing plantar pressure insole systems often lack the resolution needed for detailed foot analysis. To address this, a real-time insole system is presented with 253 high-density resistive pressure sensors (4 sensors per cm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) for each foot with a wireless transfer rate of 60 Hz. In addition, our work combines the insole hardware with a custom convolutional neural network (CNNs) and long short-term memory (LSTM) model to predict six lower body joint landmark positions. The prediction achieves a coefficient of determination (R<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>) of 0.83 and a mean squared error (MSE) ranging from 7.0e-4 to 9.6e-4. With an inference time of 0.6 ms, this system provided accurate, high-resolution plantar foot pressures and insights into 3D joint movements in the lower body. It is a promising tool for applications in rehabilitation and sports performance optimisation.