Design and Development of a Neural Network Based Novel Sensorized Insole for Ground Reaction Force and Center of Pressure Estimation

A fully wearable solution estimating both kinematics and dynamics of motor tasks, including walking, running, and balance, would improve performance in clinical, rehabilitation, and sports settings. However, measuring GRF and CoP in a real-world setting remains an open challenge. Recently, sensorized insoles have become increasingly widespread, however, there are still many gaps in the determination of dynamic quantities. In this study, a novel composite insole with six FSRs was introduced to calculate GRF and CoP. The prototype was made with a commercial insole at the base, equipped with six FSRs and covered with two layers of silicone with different stiffness. The calibration curve was use to estimate GRF and CoP directly, and a machine learning approach was used to estimate them indirectly. As a first performance test, we conducted static standing trials. Due to the surface coverage of the sensors and the nature of the activity under study, the direct method showed underestimation of the GRF value. On the other hand, the ANN showed a superior performance in estimating GRF and CoP in subjects with regular foot but degrading in subjects with unusual foot. Preliminary results obtained on balance task are encouraging for future expansions of the training dataset, including other motor tasks.