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

Carbon Skeletons01:12

Carbon Skeletons

110.5K
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...
110.5K
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

56.9K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
56.9K

You might also read

Related Articles

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

Sort by
Same author

The endoplasmic reticulum is a target organelle for trivalent dimethylarsinic acid (DMAIII)-induced cytotoxicity.

Toxicology and applied pharmacology·2012
Same author

(E)-1-{4-[Bis(4-bromo-phen-yl)meth-yl]piperazin-1-yl}-3-(4-eth-oxy-phen-yl)prop-2-en-1-one.

Acta crystallographica. Section E, Structure reports online·2012
Same author

(E)-1-{4-[Bis(4-bromo-phen-yl)meth-yl]piperazin-1-yl}-3-(4-methyl-phen-yl)prop-2-en-1-one.

Acta crystallographica. Section E, Structure reports online·2012
Same author

(E)-3-(1,3-Benzodioxol-5-yl)-1-{4-[bis-(4-meth-oxy-phen-yl)meth-yl]piperazin-1-yl}prop-2-en-1-one.

Acta crystallographica. Section E, Structure reports online·2012
Same author

Economic evaluation of first-line treatments for metastatic renal cell carcinoma: a cost-effectiveness analysis in a health resource-limited setting.

PloS one·2012
Same author

Metabolism studies of casticin in rats using HPLC-ESI-MS(n).

Biomedical chromatography : BMC·2012

Related Experiment Video

Updated: Sep 16, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.0K

Lightweight and efficient skeleton-based sports activity recognition with ASTM-Net.

Bin Wu1, Mei Xue2, Ying Jia3

  • 1College of Light Textile and Chemical Engineering, Binzhou Polytechnic, Shandong, China.

Plos One
|July 8, 2025
PubMed
Summary
This summary is machine-generated.

ASTM-Net advances human activity recognition using skeletal data by dynamically modeling spatial affinities and temporal dependencies. This novel approach significantly improves accuracy while reducing computational costs and enhancing robustness to occlusions.

More Related Videos

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
07:25

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

7.0K
An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

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

8.1K

Related Experiment Videos

Last Updated: Sep 16, 2025

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment
06:49

Evaluation of a Smartphone-based Human Activity Recognition System in a Daily Living Environment

Published on: December 11, 2015

9.0K
An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor
07:25

An Objective and Child-friendly Assessment of Arm Function by Using a 3-D Sensor

Published on: February 12, 2018

7.0K
An Inertial Measurement Unit Based Method to Estimate Hip and Knee Joint Kinematics in Team Sport Athletes on the Field
06:52

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

8.1K

Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Machine Learning

Background:

  • Human Activity Recognition (HAR) is crucial for video understanding, with skeletal data offering robustness against environmental variations.
  • Existing Graph Convolutional Network (GCNN) methods struggle with dynamic spatial relationships and integrating spatial-temporal information for complex actions.

Purpose of the Study:

  • To introduce ASTM-Net, a novel Activity-aware SpatioTemporal Multi-branch graph convolutional network designed to overcome limitations in skeletal HAR.
  • To enhance the capture of dynamic node affinities and the interplay between spatial and temporal features for improved action recognition.

Main Methods:

  • Developed the Activity-aware Spatial Graph convolution Module (ASGM) to dynamically model Activity-Aware Adjacency Graphs (3A-Graphs) by fusing multiple graph types.
  • Introduced the Temporal Multi-branch Graph convolution Module (TMGM) using parallel branches with dilated convolutions and pooling for efficient temporal modeling.
  • Integrated ASGM and TMGM to jointly capture spatio-temporal information with reduced computational complexity.

Main Results:

  • ASTM-Net achieved superior performance on benchmark datasets (NTU-RGB+D, NTU-RGB+D 120, Toyota Smarthome), outperforming state-of-the-art methods.
  • Demonstrated significant reductions in parameters (51.9%) and FLOPs (49.7%) compared to MST-GCNN-ALLs, while improving accuracy by 0.82%.
  • Showcased high accuracy (86.94%) under 30% random node occlusion, indicating robustness.

Conclusions:

  • ASTM-Net effectively addresses the limitations of existing GCNNs in skeletal HAR by dynamically modeling spatial affinities and temporal dependencies.
  • The proposed multi-branch architecture offers a parameter-efficient and computationally effective solution for complex activity recognition tasks.
  • ASTM-Net represents a significant advancement in skeletal-based Human Activity Recognition, offering improved accuracy, efficiency, and robustness.