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The use of computer based classification methods for spastic hemiplegia (SH) is a relatively new approach. Before and still, the classification is primarily done by specialized physicians through manual template matching on kinematics plots of the sagittal plane data from the affected limb generated by gait laboratories. In the past, several methods for computer based automation of this classification have been attempted. This paper introduces the use of support vector machine (SVM) as a model that contributes to this process. The results obtained from the use of SVM are quite efficient considering the data set utilized involves patients from an array of ages and both sex. It should be mentioned that even though the number of patients is not trivial; the percentages of accurate classification represented are promising. Since the SVM method improves its accuracy with an increased number of training cases, this approach has the advantage of becoming more accurate with time. Many more significant discoveries are expect from the introduction of SVM in the analysis of SH and further studies of gait related pathologies are expected to contribute as well.

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Myelomeningocele results from failure of the neural tube to close in the developing fetus and is associated with neurological impairment (Incidence 1:1000 births). The level of the anatomic lesion generally correlates with the neurological deficit and ranges from complete paralysis to minimal or in some cases no motor involvement. Myelomeningocele or Spina bifida can lead to health problems, physical disabilities, and learning problems. Most commonly, associated with paralysis of the lower extremities and neurogenic bladder. Treatment requires multidisciplinary participation. The functional classification that concerns us in this review includes three types and were obtained through gait analysis. Keywords: Spina bifida; instrumental; gait analysis; kinematics and kinetics; orthotics