Walking Speed Estimation From a Wearable Insole Pressure System Using a Bayesian Regularized Back Propagation Neural Network

Abstract In this study, we introduced a machine learning method for estimating human walking speed using plantar pressure and acceleration data. A pressure-derivative based with pretest feature selection method was proposed to extracted speed-related features from plantar pressure sensors. The maximum, minimum and standard deviation of acceleration data were also selected as neural network inputs. To improve the generalization ability of the neural network, a Bayesian regularization method was adopted. To validate the performance of the proposed method, experiments were conducted under seven different walking speeds. The results show that a strong linear correlation (R = 0.995) exists between the estimated and the actual walking speeds. The average error of the proposed method is 0.003 ± 0.043 m/s (mean ± root mean square error), which is better than previous works. The desirable performance of the proposed method proves that including the speed-related information of both stance and swing phase is beneficial for improving the accuracy of walking speed estimation.