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Knee Joint

Knee Joint

The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris group...

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Updated: Jun 25, 2026

Comparative Analysis of Lower Limb Kinematics between the Initial and Terminal Phase of 5km Treadmill Running

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Published on: July 17, 2020

Differences in cutting knee mechanics based on principal components analysis.

Kristian M O'Connor¹, Michael C Bottum

¹Department of Human Movement Sciences, Neuromechanics Laboratory, University of Wisconsin-Milwaukee, Milwaukee, WI 53201l, USA. krisocon@uwm.edu

Medicine and Science in Sports and Exercise

|March 12, 2009

Summary

This summary is machine-generated.

Principal component analysis (PCA) revealed subtle gender differences in knee mechanics during cutting tasks, offering new insights into injury risk factors for female athletes. This advanced analysis identified movement patterns not seen with traditional methods.

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Area of Science:

Biomechanics
Sports Medicine
Orthopedics

Background:

Female athletes experience higher knee injury rates than males.
Traditional analysis of knee injury risk focuses on discrete variables.
Principal Component Analysis (PCA) offers a novel approach to analyze complex movement waveforms.

Purpose of the Study:

To investigate gender differences in knee joint mechanics during cutting maneuvers using PCA.
To compare PCA findings with traditional discrete variable analysis for injury risk assessment.

Main Methods:

Recreational male and female athletes performed unanticipated cutting tasks.
Three-dimensional joint dynamics were captured and analyzed using PCA on angle and moment waveforms.
Gender differences in principal component (PC) scores were evaluated using MANOVA.

Main Results:

Discrete analysis showed less knee flexion in females.
PCA identified reduced internal rotation and a greater peak adduction moment in females during late stance.
PCA revealed less variability in sagittal and frontal plane moments for females.

Conclusions:

PCA detected gender-specific movement patterns and variability missed by discrete analysis.
PCA shows potential for identifying subtle knee injury risk factors in athletes.
This advanced analysis may enhance understanding of biomechanical differences contributing to knee injuries.