Multi-joint gait clustering for children and youth with diplegic cerebral palsy

Autor: Ion Robu, Gregor Kuntze, Gina Ursulak, Carolyn A. Emery, Simon Goldstein, Nicole Bowal, Alberto Nettel-Aguirre
Rok vydání: 2018
Předmět:
Male
030506 rehabilitation
Kinematics
Physiology
Knees
lcsh:Medicine
Walking
Cohort Studies
Mathematical and Statistical Techniques
0302 clinical medicine
Gait (human)
Skeletal Joints
Medicine and Health Sciences
Cluster Analysis
lcsh:Science
Child
Musculoskeletal System
Mathematics
Multidisciplinary
Physics
Classical Mechanics
Gross Motor Function Classification System
Biomechanical Phenomena
medicine.anatomical_structure
Child
Preschool
Physical Sciences
Legs
Female
Anatomy
Gait Analysis
0305 other medical science
Research Article
Adult
medicine.medical_specialty
Adolescent
Research and Analysis Methods
Pelvis
Young Adult
03 medical and health sciences
Physical medicine and rehabilitation
medicine
Humans
Hip
Biological Locomotion
Cerebral Palsy
lcsh:R
Ankles
Biology and Life Sciences
Sagittal plane
Preferred walking speed
Body Limbs
Gait analysis
lcsh:Q
K Means Clustering
Ankle
030217 neurology & neurosurgery
Diplegic cerebral palsy
Zdroj: PLoS ONE
PLoS ONE, Vol 13, Iss 10, p e0205174 (2018)
ISSN: 1932-6203
2007-2015
DOI: 10.1371/journal.pone.0205174
Popis: Background Clinical management of children and youth with cerebral palsy (CP) is increasingly supported by computerized gait analysis. Methods have been developed to reduce the complexity of interpreting biomechanical data and quantify meaningful movement patterns. However, few methods are inclusive of multiple joints and planes of motion, and consider the entire duration of gait phases; potentially limiting insight into this heterogeneous pathology. The objective of this study was to assess the implementation of k-means clustering to determine clusters of participants with CP based on multi-joint gait kinematics. Methods Barefoot walking kinematics were analyzed for a historical cohort (2007-2015) of 37 male and female children and youth with spastic diplegic CP [male n = 21; female n = 16; median age = 12 (range 5-25) years; Gross Motor Function Classification System Level I n = 17 and Level II n = 20]. Mean stance phase hip (sagittal, coronal, transverse), knee (sagittal), and ankle (sagittal) kinematics were time (101 data points), mean and range normalized. Normalized kinematics data vectors (505 data points) for all participants were then combined in a single data matrix M (37x505 data points). K-means clustering was conducted 10 times for all data in M (2-5 seeds, 50 repetitions). Cluster quality was assessed using the mean Silhouette value ([Formula: see text]) and cluster repeatability. The mean kinematic patterns of each cluster were explored with respect to a dataset of normally developing (ND) children using Statistical Parametric Mapping (SPM, alpha 0.05). Differences in potentially confounding variables (age, height, weight, walking speed) between clusters (C) were assessed individually in SPSS (IBM, USA) using Kruskal-Wallis H tests (alpha 0.05). Results Four clusters (n1 = 5, n2 = 12, n3 = 12, n4 = 8) provided the largest possible data separation based on high cluster repeatability (96.8% across 10 repetitions) and comparatively greater cluster quality [[Formula: see text] (SD), 0.275 (0.152)]. Participant data with low cluster quality values displayed a tendency toward lower cluster allocation repeatability. Distinct kinematic differences between clusters and ND data were observable. Specifically, C1 displayed a unique continuous hip abduction and external rotation pattern. In contrast, participants in C2 moved from hip adduction (loading response) to abduction (mid to terminal stance) and featured a unique ankle plantarflexor pattern during pre-swing. C3 was characterized by gait deviations in the sagittal plane of the hip, knee and ankle only. C4 displayed evidence for the most substantial hip and knee extension, and ankle plantarflexion deficit from midstance to pre-swing. Discussion K-means clustering enabled the determination of up to four kinematic clusters of individuals with spastic diplegic CP using multi-joint angles without a priori data reduction. A cluster boundary effect was demonstrated by the Silhouette value, where data with values approaching zero were more likely to change cluster allocation. Exploratory analyses using SPM revealed significant differences across joints and between clusters indicating the formation of clinically meaningful clusters. Further work is needed to determine the effects of including further topographical classifications of CP, additional biomechanical data, and the sensitivity to clinical interventions to assess the potential for informing clinical decision-making.
Databáze: OpenAIRE
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