Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

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...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

The effects of intravenous iron on mobility in older adults following hip fracture surgery: protocol for a multicentre, parallel group, randomised controlled trial - the IronHip trial.

BMJ open·2026

Same author

Coil Orientation in Transcranial Magnetic Stimulation Affects Motor-evoked Potential Size more than its Timing or Waveform Shape.

Brain topography·2026

Same author

Intensive diet or knee arthroplasty for severe knee osteoarthritis: The inka randomised trial.

Osteoarthritis and cartilage·2026

Same author

Dynamic Knee Stability and Movement Strategy During a Forward Lunge After Anterior Cruciate Ligament Reconstruction: A Comparison of Individuals With and Without Knee Pain.

Journal of applied biomechanics·2026

Same author

Neural Network-Driven Finite Element Modeling for Estimating Knee Joint Cartilage Mechanical Responses.

Annals of biomedical engineering·2026

Same author

Technology-assisted physical activity among hospitalized medical patients: a cluster randomized trial.

European journal of preventive cardiology·2026

Search research articles

Related Experiment Video

Updated: Jun 23, 2026

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Published on: May 12, 2016

Predicting Knee Joint Contact Force Peaks During Gait Using a Video Camera or Wearable Sensors.

Jere Lavikainen^1,2, Lauri Stenroth³, Paavo Vartiainen³

¹Department of Technical Physics, University of Eastern Finland, Kuopio, Finland. jere.lavikainen@uef.fi.

Annals of Biomedical Engineering

|August 3, 2024

Summary

Estimating knee joint loading is now accessible outside labs. Artificial neural networks predict loading using simple inputs from video cameras and IMUs, matching motion capture accuracy.

Keywords:

Artificial neural networks Computer vision Contact force Gait analysis Inertial measurement units Knee joint OpenPose

More Related Videos

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

Related Experiment Videos

Last Updated: Jun 23, 2026

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Substantiating Appropriate Motion Capture Techniques for the Assessment of Nordic Walking Gait and Posture in Older Adults

Published on: May 12, 2016

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field

Published on: May 26, 2020

Area of Science:

Biomechanics
Biomedical Engineering
Artificial Intelligence

Background:

Estimating knee joint loading is crucial for managing degenerative joint diseases.
Current methods rely on specialized motion capture (MOCAP) and expert analysis.
There's a need for more accessible knee loading estimation techniques.

Purpose of the Study:

To develop accessible methods for estimating knee joint loading using artificial neural networks (ANNs).
To predict knee joint loading peaks using simple input predictors.
To evaluate the feasibility of using non-specialized data for knee loading prediction.

Main Methods:

Trained feedforward ANNs to predict knee joint loading peaks from basic subject data (mass, height, age, sex, walking speed, knee flexion angle).
Collected independent data using video cameras (VC) and inertial measurement units (IMUs) alongside MOCAP.
Quantified ANN prediction accuracy using VC and IMU data compared to MOCAP data.

Main Results:

Achieved prediction accuracies with portable modalities (VC, IMUs) comparable to MOCAP data.
Reported normalized root mean square errors between 0.13 and 0.37.
Observed correlations between predicted and reference loading peaks ranging from 0.65 to 0.91.

Conclusions:

Video cameras (VC) and inertial measurement units (IMUs) can provide predictors for estimating knee joint loading outside laboratory settings.
These portable methods offer a viable alternative to traditional motion capture.
Further research should focus on the usability of these methods in real-world, out-of-laboratory environments.