Abstract
Background: Interpretation of gait data obtained from modern 3D gait analysis is a challenging and time consuming task. The aim of this study was to create neural network modelswhich can recognise the gait patternsfrom pre- and post-treatment and the normal ones. Neural network is a method which workson the principle of learning from experience and then uses the obtained knowledge to predict the unknowns.Methods: Twenty-eight patients with cerebral palsy were recruited as subjects whose gait was analysed in pre and post-treatment. A group of twenty-six normal subjects also participated in this study as control group.All subjects’ gait was analysed using Vicon Nexus®to obtain the gait parameters and kinetic and kinematicparameters of hip, knee and ankle joints in three planes of both limbs. The gait data was used as input to create neural network models. A total of approximately 300 trials were split into 70% and 30% to train and test the models, respectively. Different modelswere built using different parameters. The gait modes were categorised asthree patterns, i.e.,normal, pre-and post-treatments.Results: The results showed that the models using all parameters or using the joint angles and moments could predict the gait patterns with approximately 95% accuracy. Some of the models e.g.,the models using joint power and moments,had lower rate in recognition of gait patterns with approximately 70–90% successful ratio. Conclusion: Neural network modelscan be used in clinical practice to recognise the gait pattern for cerebral palsy patients.
Dr Jan Muhammad,, Sheila Gibbs, , Rami Abboud, , Sambandam Anand, , Weijie Wang. (2015) GAIT PATTERN RECOGNITION IN CEREBRAL PALSY PATIENTS USING NEURAL NETWORK MODELLING, JOURNAL OF AYUB MEDICAL COLLEGE ABBOTTABAD, Volume 27, Issue 4.
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