Validation of a GPS Sensor Vest for Predicting Ground Reaction Force

Abstract

This study presents a novel approach to estimate Ground Reaction Forces (GRFs) during athletic exercises using wearable GPS sensor technology and artificial neural networks (ANNs). GRFs have typically required the utilization of heavy force platforms in carefully controlled laboratory conditions in order to fully comprehend the biomechanical stresses that athletes experience during training and competition. In contrast, this study makes use of wearable sensors' usefulness to capture real-time data in realistic training and gaming contexts. With excellent R2 scores and small Root Mean Squared Error (RMSE) values for a variety of trials, the constructed ANN model demonstrates impressive accuracy. These results demonstrate that the ANN can successfully establish the complex link between GPS sensor data and GRFs, allowing accurate predictions of these forces during dynamic motions. These results demonstrate that the ANN can successfully establish the complex link between GPS sensor data and GRFs, allowing accurate predictions of these forces during dynamic motions. This work highlights the potential for practical sports science applications, providing coaches, athletes, and medical teams with invaluable information about enhancing performance and reducing injury risks.