Objective A practical and highly accurate algorithm for dynamic monitoring of plantar pressure was proposed, the magnitude of vertical ground reaction force (vGRF) during walking was measured by a capacitive insole sensor, and reliability of the prediction accuracy was verified. Methods Four healthy male subjects were require to wear capacitive insole sensors, and their fast walking and slow walking data were collected by Kistler three-dimensional (3D) force platform. The data collected by the capacitive insole sensors were pixelated, and then the processed data were fed into a residual neural network, ResNet18, to obtain high-precision vGRF. Results Compared with analysis of the data collected from Kister force platform, the normalized root mean square error (NRMSE) for fast walking and slow walking were 8.40% and 6.54%, respectively, and the Pearman correlation coefficient was larger than 0.96. Conclusions This study provides a novel algorithm for dynamic measurement of GRF in mobile scenarios, which can be used for estimation of complete GRF outside the laboratory without being constrained by the number and location of force plates. Potential application areas include gait analysis and efficient capture of pathological gaits.