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Updated: Jun 3, 2025
Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
Published on: March 4, 2018
Multimodal fuzzy logic-based gait evaluation system for assessing children with cerebral palsy
Saleh Massoud1, Ebrahim Ismaiel2, Rasha Massoud3
1Department of Biomedical Engineering, Faculty of Mechanical and Electrical Engineering, Damascus University, Damascus 86, Syria.
Insights
A new fuzzy logic system-based gait index (FLS-GIS) accurately scores gait patterns in children with cerebral palsy (CP). Post-surgery assessments show significant improvements, bringing CP gait closer to typical patterns.
Area of Science:
- Biomechanics
- Computational Intelligence
- Pediatric Orthopedics
Background:
- Gait analysis is vital for diagnosing and treating motor disorders like cerebral palsy (CP).
- Current gait assessment methods may lack the precision needed for individualized CP treatment.
- Surgical interventions, such as Achilles tendon lengthening, aim to improve gait in children with CP.
Purpose of the Study:
- To introduce a novel multimodal fuzzy logic system-based gait index (FLS-GIS) for quantitative gait assessment.
- To evaluate the effectiveness of the FLS-GIS in assessing surgical outcomes in children with CP.
- To compare the performance of FLS-GIS against traditional gait indices.
Main Methods:
- Development of a fuzzy logic system-based gait index (FLS-GIS) using hierarchical feature fusion.
- Implementation of two FLS types: FLS-GIS-T1 and FLS-GIS-T2, analyzing spatial and temporal gait features.
- Evaluation of gait parameters in healthy children and children with CP before and after Achilles tendon lengthening surgery.
Main Results:
- The FLS-GIS provides numerical scores for gait patterns, distinguishing between healthy and CP subjects.
- Post-surgery gait parameters in children with CP showed significant improvements, moving towards typical gait patterns.
- Both FLS-GIS-T1 and FLS-GIS-T2 demonstrated statistically significant improvements (p < 0.05 and p < 0.001, respectively) compared to pre-surgery evaluations and traditional indices.
Conclusions:
- The proposed FLS-GIS is a robust and standardized tool for clinicians to evaluate gait in children with CP.
- The FLS-GIS offers consistent gait assessment across diverse conditions due to its fixed value range.
- This system aids in objectively measuring the efficacy of surgical interventions for CP.
Abstract:
Gait analysis is crucial for identifying functional deviations from the normal gait cycle and is essential for the individualized treatment of motor disorders such as cerebral palsy (CP). The primary contribution of this study is the introduction of a multimodal fuzzy logic system-based gait index (FLS-GIS), designed to provide numerical scores for gait patterns in both healthy children and those with CP, before and after surgery. This study examines and evaluates the surgical outcomes in children with CP who have undergone Achilles tendon lengthening. The FLS-GIS utilizes hierarchical feature fusion and fuzzy logic models to systematically evaluate and score gait patterns, focusing on spatial and temporal features across the hip, knee, and ankle joints. The two FLS types-1 (FLS-GIS-T1) and type-2 (FLS-GIS-T2) indices, respectively, were implemented to comprehensively study gait profiles. Starting with the gait parameters of all subjects, the changes in gait parameters in post-surgery children reflect significant improvements in gait dynamics, bringing walking patterns in CP children closer to those of their typically healthy peers. Both FLS-GIS-T1 and FLS-GIS-T2 demonstrated significant improvements in post-surgery evaluations compared to pre-surgery assessments, with p values < 0.05 and < 0.001, respectively, when compared to traditional indices. The proposed FLS-based index offers clinicians a robust and standardized gait evaluation tool, characterized by a fixed range of values, enabling consistent assessment across various gait conditions.
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