Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Labeling DNA Probes03:31

Labeling DNA Probes

7.7K
DNA probes are fragments of DNA labeled with a reporter tag to enable their detection or purification. The resulting labeled DNA probes can then hybridize to target nucleic acid sequences through complementary base-pairing, and may be used to recover or identify these regions.
Radioisotopes, fluorophores, or small molecule binding partners like biotin or digoxigenin, are the most widely used reporter tags for labeling DNA probes. These labels can be attached to the probe DNA molecule via...
7.7K
Immunogold Electron Microscopy01:20

Immunogold Electron Microscopy

4.9K
Immunoelectron microscopy utilizes immunogold labeling of endogenous proteins with specific antibodies to detect and localize these proteins in cells and tissues. The procedure provides insights into the distribution and quantification of protein under different stimulation conditions offering clues about their functions. Conjugating highly electron-dense gold particles with primary or secondary antibodies allow antigen detection on and within cells, with high resolution and specificity.
4.9K

You might also read

Related Articles

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

Sort by
Same author

Patterns of Muscle Health in Single- and Multi-Site Chronic Pain: A UK Biobank Normative Modeling Study.

medRxiv : the preprint server for health sciences·2026
Same author

Neural Shape Modeling Reveals Early and Progressive Femoral Bone Shape and Cartilage Thickness Changes After Anterior Cruciate Ligament Reconstruction.

medRxiv : the preprint server for health sciences·2026
Same author

The menstrual cycle through the lens of a wearable device: insights into physiology, sleep, and cycle variability.

NPJ digital medicine·2026
Same author

Integrating Machine Learning with Musculoskeletal Simulation Improves OpenCap Video-Based Dynamics Estimation.

IEEE transactions on bio-medical engineering·2026
Same author

BMI and Varus Malalignment Compound to Define a High-Risk Phenotype for Compartment-Specific Knee Osteoarthritis Progression.

medRxiv : the preprint server for health sciences·2026
Same author

Smartphone video-based knee extension moments during chair rise relate to MRI measures of muscle function.

medRxiv : the preprint server for health sciences·2026

Related Experiment Video

Updated: May 2, 2026

Movement Retraining using Real-time Feedback of Performance
08:16

Movement Retraining using Real-time Feedback of Performance

Published on: January 17, 2013

13.2K

Marker Data Enhancement for Markerless Motion Capture.

Antoine Falisse, Scott D Uhlrich, Akshay S Chaudhari

    IEEE Transactions on Bio-Medical Engineering
    |March 3, 2025
    PubMed
    Summary
    This summary is machine-generated.

    This study developed an improved marker enhancer for human pose estimation, significantly boosting movement analysis accuracy and generalizability for researchers using the OpenCap system.

    More Related Videos

    3D Kinematic Gait Analysis for Preclinical Studies in Rodents
    10:19

    3D Kinematic Gait Analysis for Preclinical Studies in Rodents

    Published on: August 3, 2019

    10.6K
    Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents
    04:37

    Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

    Published on: July 6, 2022

    2.3K

    Related Experiment Videos

    Last Updated: May 2, 2026

    Movement Retraining using Real-time Feedback of Performance
    08:16

    Movement Retraining using Real-time Feedback of Performance

    Published on: January 17, 2013

    13.2K
    3D Kinematic Gait Analysis for Preclinical Studies in Rodents
    10:19

    3D Kinematic Gait Analysis for Preclinical Studies in Rodents

    Published on: August 3, 2019

    10.6K
    Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents
    04:37

    Comprehensive Understanding of Inactivity-Induced Gait Alteration in Rodents

    Published on: July 6, 2022

    2.3K

    Area of Science:

    • Biomechanics
    • Computer Vision
    • Machine Learning

    Background:

    • Open-source human pose estimation models offer scalable, low-cost movement analysis from videos.
    • Existing models often detect sparse keypoints, leading to inaccurate joint kinematics.
    • The OpenCap service uses a deep learning marker enhancer to improve keypoint accuracy but struggles with un-seen movements.

    Purpose of the Study:

    • To develop a more accurate and generalizable marker enhancer for human pose estimation.
    • To improve the measurement of joint kinematics from video data.
    • To enhance the OpenCap service's performance across a wider range of human movements.

    Main Methods:

    • Compiled marker-based motion capture data from 1176 subjects.
    • Synthesized 1433 hours of video keypoints and anatomical markers for training.
    • Evaluated the enhanced marker enhancer using benchmark and synthetic diverse movement data.

    Main Results:

    • The new marker enhancer significantly improved kinematic accuracy on benchmark movements (mean error: 4.1°).
    • It demonstrated superior generalizability to unseen movements (mean error: 4.1°) compared to the original OpenCap enhancer (mean error: 40.4°).
    • The enhanced model outperformed both raw video keypoints and the original OpenCap enhancer.

    Conclusions:

    • The developed marker enhancer shows enhanced accuracy and generalizability for diverse human movements.
    • Integration into OpenCap provides researchers with more precise movement measurements.
    • This advancement broadens the applicability of video-based motion analysis.