Fuzzy clustering of children with cerebral palsy based on temporal-distance gait parameters

Temporal-distance parameters for 88 children with the spastic diplegia form of cerebral palsy (CP) are grouped using the fuzzy clustering paradigm. The 2 features chosen for clustering are stride length and cadence which are normalized for age and leg length using a model based on a population of 68 neurologically intact children. Using information provided by the neurologically intact population and cluster validity techniques, 5 clusters for the children with cerebral palsy are identified. The 5 cluster centers represent distinct walking strategies adopted by children with cerebral palsy. Utilizing just four easily obtained parameters-stride length, cadence, leg length and age-and a small number of simple equations, it is possible to classify any child with spastic diplegia and to generate an individual's membership values for each of the 5 clusters. The clinical utility of the fuzzy clustering approach is demonstrated with pre- and post-operative test data for subjects with cerebral palsy (one neurosurgical and one orthopaedic) where changes in membership of the 5 clusters provide an objective technique for measuring improvement. This approach can be adopted to study other clinical entities where different cluster centers would be established using the algorithm provided here.