A gait nomogram used with fuzzy clustering to monitor functional status of children and young adults with cerebral palsy

Abstract

We have developed a gait nomogram based on dynamic similarity to characterize and compare neuromuscular function. We used temporal–distance data based on 669 normal participants (age range 5 to 98 years), and 78 children and young adults with spastic diplegia (43 males, 35 females; mean age 10y 8mo, standard deviation 3y 11mo, range 5 to 20y), all of whom were independent ambulators. A new statistical algorithm known as fuzzy clustering was implemented and five cluster centres were identified, each representing distinct walking strategies adopted by children with cerebral palsy. Using just three easily obtained parameters – leg length in metres, stride length in metres, and cadence in steps per minute – our program calculates a child's dimensionless step length and step frequency, generates the individual's membership values for each of the five clusters, and plots the gait nomogram. The clinical utility of our approach has been demonstrated for two test participants with spastic diplegia, using pre- and postoperative data (one neurosurgical and one orthopaedic), where changes in membership of the five clusters provide objective measures of improvement in their neuromuscular function.