This work presents a high-resolution plantar pressure measurement system designed to capture detailed foot information for gait analysis. After training, the customized regression model can estimate lower body joints positions in real-time using foot pressure data. The demonstration will feature a UK size 8 insole, equipped with 253 resistive pressure sensors wirelessly connected to the server. Visitors will have the opportunity to experience walking with the insole, observing the plantar force distribution displayed and lower body joints as predicted by the model on the computer.

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 cm2) 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 (R2) 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.