Abstract
Ankle and foot orthoses are commonly prescribed to children with cerebral palsy (CP). It is unclear whether 3D gait analysis (3DGA) provides sufficient and reliable information for clinicians to be consistent when prescribing orthoses. Data-driven modeling can probe such questions by revealing non-intuitive relationships between variables such as 3DGA parameters and gait outcomes of orthoses use. The purpose of this study was to (1) develop a data-driven model to classify children with CP according to their gait biomechanics and (2) identify relationships between orthotics types and gait patterns. 3DGA data were acquired from walking trials of 25 typically developed children and 98 children with CP with additional prescribed orthoses. An unsupervised self-organizing map followed by k-means clustering was developed to group different gait patterns based on children’s 3DGA. Model inputs were gait variable scores (GVSs) extracted from the gait profile score, measuring root mean square differences from TD children’s gait cycle. The model identified five pathological gait patterns with statistical differences in GVSs. Only 43% of children improved their gait pattern when wearing an orthosis. Orthotics prescriptions were variable even in children with similar gait patterns. This study suggests that quantitative data-driven approaches may provide more clarity and specificity to support orthotics prescription.
Highlights
Ankle–foot orthoses (AFOs) and foot orthoses are commonly prescribed to help children with cerebral palsy (CP) maintain independent mobility [1,2]
The present study used a self-organizing map (SOM) to (1) develop a data-driven model to classify children with CP according to their gait biomechanics and (2) identify relationships between orthosis types and the gait patterns revealed by the model
Pairwise comparisons of the gait variable score (GVS) distribution are displayed for the pelvis (Figure 4), hip (Figure 5), knee (Figure 6), and foot and ankle (Figure 6) for each group using box plot representation
Summary
Ankle–foot orthoses (AFOs) and foot orthoses are commonly prescribed to help children with cerebral palsy (CP) maintain independent mobility [1,2]. 3D gait analysis (3DGA) is used as part of clinical care for children with CP; gait data are typically collected for both barefoot walking and walking with the orthosis. Each orthotic design targets different aspects of the gait pattern; for example, the solid/rigid AFO primarily aims to restrict ankle plantarflexion and dorsiflexion to enable heel strike in the stance phase and toe clearance in the swing phase, whereas the PLS AFO enhances push-off power in the terminal stance by acting like a spring at the ankle [5,6]. This provides information about the effectiveness of the orthosis in positively changing the gait pattern and supports clinicians’ decisions around changes in orthotic prescription [7]. This provides information about the effectiveness of the orthosis in positively changing the gait pattern and supports clinicians’ decisions around changes in orthotic prescription [7]. 3DGA measurements are accurate, but interpretation of this complex information can be challenging [8,9]
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