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Fuzzy clustering of children with cerebral palsy based on temporal-distance gait parameters
M J O'Malley1, M F Abel, D L Damiano
1Department of Electronic and Electrical Engineering, University College, Dublin, Ireland.
Insights
Fuzzy clustering identified five distinct walking strategies in children with cerebral palsy (CP) using stride length and cadence. This method offers an objective way to classify CP patients and monitor treatment effectiveness.
Area of Science:
- Biomedical Engineering
- Clinical Biomechanics
- Pediatric Orthopedics
Background:
- Cerebral palsy (CP) affects motor skills, impacting gait in affected children.
- Objective gait analysis is crucial for understanding and managing CP-related mobility impairments.
- Current methods for gait classification in CP can be complex and lack standardization.
Purpose of the Study:
- To apply fuzzy clustering to temporal-distance gait parameters in children with spastic diplegia CP.
- To identify distinct walking strategies based on normalized stride length and cadence.
- To develop a clinically applicable method for classifying CP gait and assessing treatment outcomes.
Main Methods:
- Utilized fuzzy clustering on temporal-distance gait parameters (stride length, cadence) from 88 children with spastic diplegia CP.
- Normalized gait parameters using age and leg length based on data from 68 neurologically intact children.
- Employed cluster validity techniques to determine the optimal number of clusters.
Main Results:
- Identified five distinct clusters representing unique walking strategies in children with CP.
- Developed a classification system using four easily obtainable parameters (stride length, cadence, leg length, age).
- Demonstrated clinical utility through pre- and post-operative data, showing objective improvement measurement.
Conclusions:
- Fuzzy clustering provides an objective and efficient method for classifying gait patterns in children with spastic diplegia CP.
- The identified clusters and classification system can aid in personalized treatment and outcome assessment.
- This approach is adaptable for analyzing gait in other neurological or musculoskeletal conditions.
Abstract:
Temporal-distance parameters for 88 children with the spastic diplegia form of cerebral palsy (CP) are grouped using the fuzzy clustering paradigm. The two 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, five clusters for the children with cerebral palsy are identified. The five 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 five 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 five 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.