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

Carbon Skeletons

Carbon Skeletons

Life on Earth is carbon-based, as all macromolecules that make up living organisms contain carbon atoms. All organic compounds have a carbon backbone. Each carbon atom is tetravalent and can bond with four other atoms, making it an extraordinarily flexible component of biological molecules. Because carbon’s valence electrons are stable, it rarely becomes an ion. As the carbon chain increases in length, structural modifications such as ring structures, double bonds, and branching side...

Classification of Bones

Classification of Bones

The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...

Muscle Coordination and Action

Muscle Coordination and Action

Muscle coordination is a complex and finely tuned process essential for smooth and purposeful movements like flexion, extension, adduction, abduction, and rotation. The human body orchestrates the actions of various muscles working in concert, each with a specific role. Four functional types describe how muscles work together: agonist, antagonist, synergist, and fixator.
Agonists
Agonist muscles, often called prime movers, are the primary muscles responsible for producing a specific movement....

You might also read

Related Articles

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

Sort by

Same author

Acute changes in ankle dorsiflexor strength and fNIRS-Derived cortical activation following a single session of neuromuscular electrical stimulation in healthy older adults.

Frontiers in aging·2026

Same author

Machine learning for predicting surgical difficulty of laparoscopic total mesorectal excision for rectal cancer: integrating MR-based pelvimetry and peritoneal reflection.

Frontiers in medicine·2026

Same author

A Randomized Controlled Trial of Yizhi Kaiqiao Formula Combined With Repetitive Transcranial Magnetic Stimulation on Neurocognitive and Social Outcomes in Preschool Children With Autism Spectrum Disorder.

Developmental neurobiology·2026

Same author

Advancing high-altitude medicine: a model for the future.

Signal transduction and targeted therapy·2026

Same author

A disease-centric vision-language foundation model for precision oncology in kidney cancer.

Nature communications·2026

Same author

<sup>68</sup>Ga-Labeled LLP2A for PET Imaging of Very Late Antigen-4 in Acute Cardiac Rejection.

Molecular pharmaceutics·2026

Same journal

Relaxed Stability Conditions for Model Predictive Control of Hybrid Dynamical Systems Using Hybrid Recurrent Neural Networks.

IEEE transactions on cybernetics·2026

Same journal

An Evolutionary Algorithm Assisted by an Ensemble of Pareto-Optimal Surrogate Models.

IEEE transactions on cybernetics·2026

Same journal

A Quantum Self-Attention Neural Network Model on Quantum Circuits.

IEEE transactions on cybernetics·2026

Same journal

Semi-Explicit Solution of Some Discrete-Time Higher-Order-Cost Mean-Field-Type Control.

IEEE transactions on cybernetics·2026

Same journal

A Novel One-Step Small Object Detector for Autonomous Aerial Vehicles.

IEEE transactions on cybernetics·2026

Same journal

Online Data-Driven-Based Optimal Output Tracking Control Without Initial Stabilizing Policy.

IEEE transactions on cybernetics·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Mar 23, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Latent Max-Margin Multitask Learning With Skelets for 3-D Action Recognition.

Yanhua Yang, Cheng Deng, Dapeng Tao

IEEE Transactions on Cybernetics

|April 6, 2016

Summary

This summary is machine-generated.

This study introduces a new multitask learning model for 3-D action recognition using skeleton data. The model captures joint interdependencies for improved human action recognition performance.

More Related Videos

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Related Experiment Videos

Last Updated: Mar 23, 2026

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping

Published on: April 21, 2023

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Automated Joint Space Detection Improves Bone Segmentation Accuracy

Published on: November 28, 2025

Area of Science:

Computer Vision
Machine Learning
Human-Computer Interaction

Background:

Depth cameras enable skeleton-based human action recognition.
Existing methods often ignore crucial interdependencies between skeleton joints and action classes, limiting performance.

Purpose of the Study:

To propose a novel latent max-margin multitask learning model for 3-D action recognition.
To leverage skelets as mid-level representations for capturing joint-action class correlations.
To improve the accuracy of human action recognition from skeleton data.

Main Methods:

Developed a latent max-margin multitask learning framework.
Utilized skelets (mid-level joint representations) to describe actions.
Employed structured sparsity-inducing regularization to discover shared and preserve private information across action classes.

Main Results:

The proposed model demonstrated superior performance on three challenging depth-camera-captured datasets.
Consistent improvements over state-of-the-art approaches were observed.
Effective capture of interdependencies between skeleton data and action classes.

Conclusions:

The latent max-margin multitask learning model effectively addresses limitations in current skeleton-based action recognition.
The approach enhances recognition accuracy by exploiting joint interdependencies.
This method offers a promising direction for advanced 3-D human action recognition systems.