Kinematic patterns during walking in children: Application of principal component analysis

Area of Science:

• Biomechanics
• Human Movement Analysis
• Developmental Neuroscience

Background:

• Walking involves complex coordination of multiple body segments.
• Principal Component Analysis (PCA) is a statistical method used to reduce data dimensionality.
• Understanding kinematic patterns in children and adults is crucial for developmental studies.

Purpose of the Study:

• To identify and characterize the main multi-joint kinematic patterns during walking across different age groups.
• To investigate how these kinematic patterns change with walking velocity and age.
• To explore the potential of these patterns as markers for neural development.

Main Methods:

• Collected 3D kinematic data of children (6-9 and 10-13 years) and adults walking on a treadmill at various speeds.
• Utilized PCA to extract dominant multi-joint kinematic patterns (pmk) from the motion data.
• Analyzed the variance explained by each pattern (pm1-pm5) in relation to walking velocity (Froude number) and age group.

Main Results:

• Five principal components (pm1-pm5) explained 99.1% of the variance in walking kinematics.
• The relationship between pattern variance and walking velocity differed across components and age groups.
• Significant age-related differences were observed in sagittal plane kinematic patterns (pm1 and pm2).

Conclusions:

• Identified key, clinically interpretable kinematic patterns in human walking.
• These movement patterns show age-dependent variations, particularly in the sagittal plane.
• The identified kinematic components show promise as potential biomarkers for assessing neural development of walking.