In this study, we simplified the analysis of kinetic gait data using pattern recognition. Gait patterns were studied in 42 spastic children with cerebral palsy (age range: 3 to 17 years old), and 24 age- and sex-matched children. Gait analysis was performed using the DynoGraphy (CDG) system (Infortronic, Holland). The foot enrollment and the role of the heel or forefoot were assessed to form the gaitline. The bipedal phase was examined using a cyclogram, which is a cyclic characteristic formed by the changing position of the application point of the resultant normal force on a vertical supporting horizontal plane during motion. Based on the pattern recognition, the gait patterns of the subjects could be classified into 4 different patterns in both the gaitline and the cyclogram. The classification of the gait was parallel to the clinical evaluation of cerebral palsy obtained based on Minear's classification of daily activity (p<0.05). The correlation between the gaitline and cyclogram was also highly significant (p<0.05). The results of this study suggest that an automated pattern recognition program might provide an additional method for comprehensive gait evaluation in children with cerebral palsy.
Cyclic characteristic analysis of 9-12%Cr steel at high temperature: experiment and model
