A Leap Forward in Biomechanics: Predicting Ground Reaction Forces with Dual-Branch GRNN

Background: Accurate vertical ground reaction force (VGRF) analysis is essential for understanding biomechanics, balance, and injury prevention. However, many current predictive models face limitations in accuracy, simplicity, and applicability outside laboratory settings. Objective: This study aims to develop a predictive model for VGRF using anthropometric data to enhance the precision and applicability of biomechanical analysis. Methods: A dual-branch General Regression Neural Network (GRNN) was designed to predict VGRF at ten key points on the sole, total force, and ground contact time. The dataset included 14 selected participants. Key input variables included height, weight, BMI, navicular drop, foot size, and age. Separate branches analyzed right and left feet to improve prediction accuracy. Results: The model achieved a mean squared error (MSE) of 0.545% for total force. Compared to CNN and LSTM architectures, the accuracy of the GRNN model was significantly better while also maintaining computational efficiency. Its simple structure and fast processing capabilities make it suitable for real-time applications. Conclusion: The proposed model significantly improves VGRF prediction and is valuable for clinical diagnostics and sports science applications. Future efforts will aim to validate the model with larger datasets and integrate hybrid architectures to enhance spatiotemporal analysis.