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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...

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Related Experiment Video

Updated: Dec 6, 2025

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Published on: May 8, 2014

Estimating Knee Joint Load Using Acoustic Emissions During Ambulation.

Keaton L Scherpereel¹, Nicholas B Bolus², Hyeon Ki Jeong²

¹Woodruff School of Mechanical Engineering, Georgia Institute of Technology, Atlanta, GA, 30332, USA. keaton@gatech.edu.

Annals of Biomedical Engineering

|October 10, 2020

Summary

Joint acoustic emissions can estimate internal joint load for improved mobility. This wearable technology offers a novel approach to quantifying joint forces during daily activities.

Keywords:

Joint sounds Knee joint load Machine learning Tibiofemoral contact force

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Last Updated: Dec 6, 2025

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Oscillation and Reaction Board Techniques for Estimating Inertial Properties of a Below-knee Prosthesis

Published on: May 8, 2014

A Novel Application of Musculoskeletal Ultrasound Imaging

A Novel Application of Musculoskeletal Ultrasound Imaging

Published on: September 17, 2013

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion

Published on: April 11, 2018

Area of Science:

Biomechanics
Biomedical Engineering
Wearable Technology

Background:

Quantifying joint load is crucial for mobility improvements but current methods are not suitable for everyday use.
Developing ubiquitous methods for joint load assessment is essential for widespread application.

Purpose of the Study:

To demonstrate that joint acoustic emissions can be used to estimate internal joint load.
To explore the potential of a wearable implementation for joint load monitoring.

Main Methods:

Collected joint acoustic emissions and gait measures (electromyography, ground reaction forces, motion capture) from 11 healthy individuals during ambulation.
Used a neuromuscular model to estimate internal joint contact force.
Trained subject-specific machine learning models (XGBoost) on acoustic emission features to predict joint load.

Main Results:

Machine learning models utilizing joint acoustic emissions significantly outperformed baseline estimates (p < 0.05).
Mean Absolute Error (MAE) for seen conditions was 0.08 ± 0.01 BW, and for unseen conditions was 0.21 ± 0.05 BW.
Demonstrated a strong correlation between joint acoustic emissions and internal joint contact force.

Conclusions:

Joint acoustic emissions contain valuable information for estimating internal joint contact force.
This information is consistent and reliable for estimating joint load in unique cases.
Joint acoustic emissions offer a promising, potentially wearable, solution for ubiquitous joint load quantification.