Prediction of gait speed from plantar pressure using artificial neural networks

The goal of this study was to predict gait speed over the entire cycle in reference to plantar pressure data acquired by means of the insole-type plantar pressure measuring device (Novel Pedar-x system). To predict gait speed, the artificial neural network is adopted to develop the model to predict gait speed in the stance phase (Model I) and the model to predict gait speed in the swing phase (Model II). The predicted gait speeds were validated with actual values measured using a motion capturing system (VICON 460 system) through a five-fold cross-validation method, and the correlation coefficients (R) for the gait speed were 0.963 for normal walking, 0.978 for slow walking, and 0.950 for fast walking. The method proposed in this study is expected to be widely used clinically in understanding the progress and clarifying the cause of such diseases as Parkinsonism, strike, diabetes, etc. It is expected that the method suggested in this study will be the basis for the establishment of a new research method for pathologic gait evaluation.