A Novel Approach for Prediction of Peak Plantar Pressure during the Gait Cycle Based on the LSTM and RNN

A three-dimensional model of the ankle system was constructed based on CT scan image data with MIMICS software. This model was refined through surface smoothing and fitting operations in Geomagic software. Finite element method (FEM) was applied to simulate the forces acting on the ankle system during standing in order to generate plantar pressure nephogram. FEM results were compared with the experimental results, which illustrated a strong correlation regarding pressure peaks, thus indicating the effectiveness of the proposed model. Moreover, by changing the interaction force between the ankle system and the ground at different angles during the gait cycle, the FEM was utilized to obtain the curve of peak plantar pressure throughout the gait cycle in order to provide data for the training and testing of the AI algorithm model. The Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN) models were developed to predict peak plantar pressure during the gait cycle. After training, the mean squared error (MSE) of the LSTM model converged to [Formula: see text], and the coefficient of determination ([Formula: see text] on the test set reached 99.47%. Meanwhile, the MSE of the RNN model converged to [Formula: see text], and the [Formula: see text] on the test set reached 99.20%. Additionally, the prediction results were compared with experimental data to evaluate the validity of both models. The prediction accuracy of the two algorithms was evaluated based on the [Formula: see text]value. The results show that both models’ predictions effectively reflect variations in peak plantar pressure, with the LSTM model proving to be more accurate for predicting peak plantar pressure throughout the gait cycle. Moreover, when taking the overall computation time into account, the RNN model demonstrates significantly higher efficiency in general.