Predicting vertical and shear ground reaction forces during walking and jogging using wearable plantar pressure insoles

Background: The development of plantar pressure insoles has made them a potential replacement for force plates. These wearable devices can measure multiple steps and might be used outside of the lab environment for rehabilitation and evaluation of sport performance. However, they can only measure the normal force which does not completely represent the vertical ground reaction force (GRF). In addition, they are not able to measure shear forces which play an import role in the dynamic performance of individuals. Indirect approaches might be implemented to improve the accuracy of the force estimated by plantar pressure systems. Research question: The aim of this study was to predict the vertical and shear components of ground reaction force from plantar pressure data using recurrent neural networks. Methods: GRF and plantar pressure data were collected from sixteen healthy individuals during 10 trials of walking and five trials of jogging using Bertec force plates and FScan plantar pressure insoles. A long short-term memory (LSTM) neural network was built to consider the time dependency of pressure and force data in predictions. The data were split into three subsets of train, to train the LSTM model, evaluate, to optimize the model hyperparameters, and test sets, to assess the accuracy of the model predictions. Results: The results of this study showed that our LSTM model could accurately predict the shear and vertical GRF components during walking and jogging. The predictions were more accurate during walking compared to jogging. In addition, the predictions of mediolateral force had higher error and lower correlation compared to vertical and anteroposterior components. Significance: The LSTM model developed in this study may be an acceptable option for accurate estimation of GRF during outdoor activities which can have significant impacts in rehabilitation, sport performance, and gaming.