Healthy Knee Kinematic Phenotypes Identification Based on a Clustering Data Analysis

The purpose of this study is to identify healthy phenotypes in knee kinematics based on clustering data analysis. Our analysis uses the 3D knee kinematics curves, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation, measured via a KneeKG™ system during a gait task....

Full description

Saved in:
Bibliographic Details
Published in:Applied sciences 2021-12, Vol.11 (24), p.12054
Main Authors: Mezghani, Neila, Soltana, Rayan, Ouakrim, Youssef, Cagnin, Alix, Fuentes, Alexandre, Hagemeister, Nicola, Vendittoli, Pascal-André
Format: Article
Language:English
Subjects:
Arthritis
Asymptomatic
Biomechanics
Clustering
clustering analysis
Data analysis
Data collection
Data processing
Gait
gait analysis
Genotype & phenotype
healthy subjects
Hierarchies
kinematic data phenotype
Kinematics
Knee
Neural networks
Osteoarthritis
Phenotypes
Representations
Standardization
Statistical analysis
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The purpose of this study is to identify healthy phenotypes in knee kinematics based on clustering data analysis. Our analysis uses the 3D knee kinematics curves, namely, flexion/extension, abduction/adduction, and tibial internal/external rotation, measured via a KneeKG™ system during a gait task. We investigated two data representation approaches that are based on the joint analysis of the three dimensions. The first is a global approach that is considered a concatenation of the kinematic data without any dimensionality reduction. The second is a local approach that is considered a set of 69 biomechanical parameters of interest extracted from the 3D kinematic curves. The data representations are followed by a clustering process, based on the BIRCH (balanced iterative reducing and clustering using hierarchies) discriminant model, to separate 3D knee kinematics into homogeneous groups or clusters. Phenotypes were obtained by averaging those groups. We validated the clusters using inter-cluster correlation and statistical hypothesis tests. The simulation results showed that the global approach is more efficient, and it allows the identification of three descriptive 3D kinematic phenotypes within a healthy knee population.
ISSN:2076-3417
2076-3417
DOI:10.3390/app112412054