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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

227
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
227
Fixed Action Patterns01:06

Fixed Action Patterns

16.8K
A fixed action pattern (FAP) is a specific, hard-wired sequence of behaviors that occurs in response to an external stimulus, called a sign stimulus. The behavior is “fixed” because it is essentially unchangeable—proceeding similarly across individuals of a species every time it occurs.
16.8K
Carbon Skeletons01:12

Carbon Skeletons

111.9K
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...
111.9K
Generation of Action Potential in Skeletal Muscles01:24

Generation of Action Potential in Skeletal Muscles

7.1K
Every cell in the body maintains a membrane potential due to an uneven distribution of positive and negative charges across its plasma membrane. The membrane potential is measured in millivolts and quantifies the difference in charge across the membrane.
Like neurons, muscle cells are also regarded as excitable due to their capacity to change in response to stimuli, primarily due to voltage-gated ion channels embedded in their plasma membranes, which get activated by alterations in the...
7.1K
Muscle Coordination and Action01:24

Muscle Coordination and Action

2.5K
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....
2.5K

You might also read

Related Articles

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

Sort by
Same author

An Integrated Zero-Trust and Real-Time Detection Scheme for DDoS Protection in 5G IoT Systems.

Sensors (Basel, Switzerland)·2026
Same author

Denoising Respiratory Sinus Arrhythmia of Pulse-to-Pulse Interval Signals Extracted from Photoplethysmogram with an Autoregressive Moving Average Model.

Sensors (Basel, Switzerland)·2026
Same author

Precision improvement for indoor positioning based on fuzzy inference system with ultra-wideband wireless communications.

PloS one·2026
Same author

Hybrid CNN-LSTM Model for Evaluating Heart Rate Variability from Pulse-to-Pulse Intervals.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

Respiratory Rate Sensing for a Non-Stationary Human Assisted by Motion Detection.

Sensors (Basel, Switzerland)·2025
Same author

Using a Bodily Weight-Fat Scale for Cuffless Blood Pressure Measurement Based on the Edge Computing System.

Sensors (Basel, Switzerland)·2024
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Nov 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

736

Using Direct Acyclic Graphs to Enhance Skeleton-Based Action Recognition with a Linear-Map Convolution Neural

Tan-Hsu Tan1, Jin-Hao Hus1, Shing-Hong Liu2

  • 1Department of Electrical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.

Sensors (Basel, Switzerland)
|May 5, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a faster human activity recognition system using skeleton data and a linear-map convolutional neural network (CNN). The method enhances monitoring for elderly care by improving computational speed and accuracy in recognizing actions from RGB videos.

Keywords:
action recognitiondirect acyclic graphlinear-map convolutional neural networkspatial featuretemporal feature

More Related Videos

Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

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

Published on: April 21, 2023

1.9K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.6K

Related Experiment Videos

Last Updated: Nov 6, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

736
Estimation of Contact Regions Between Hands and Objects During Human Multi-Digit Grasping
09:41

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

Published on: April 21, 2023

1.9K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.6K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Human activity recognition (HAR) is crucial for remote elderly monitoring, aiming to reduce home care costs.
  • Video sensors offer a feasible solution for in-home monitoring systems.
  • Existing HAR methods often require large datasets and significant computational resources.

Purpose of the Study:

  • To develop an efficient and accurate HAR system using RGB videos for elderly monitoring.
  • To leverage skeleton data and a novel CNN architecture to reduce training data requirements.
  • To improve the speed and accuracy of action recognition compared to traditional methods.

Main Methods:

  • Employed skeleton data extracted from RGB videos to represent posture information, reducing the need for extensive training data.
  • Utilized a two-stream method incorporating spatial and motion features from skeleton sequences.
  • Developed a linear-map convolutional neural network (CNN) with a two-dimensional linear map for channel adjustment and action recognition.
  • Integrated direct acyclic graph (DAG) matrices to represent skeletal joint relationships, enhancing feature extraction.

Main Results:

  • Achieved high performance metrics: precision (86.9%-94.8%), recall (86.1%-94.7%), specificity (99.9%), F1-score (86.3%-94.7%), and accuracy (99.5%-99.9%) across cross-subject and cross-view evaluations.
  • Demonstrated significantly faster computation speeds compared to traditional single-frame convolution methods.
  • Validated the model using the NTU RGB+D database.

Conclusions:

  • The proposed skeleton-based HAR system with a linear-map CNN offers a practical and efficient solution for real-life action recognition.
  • The method shows significant potential for applications in elderly monitoring and other HAR-related fields.
  • Combining skeleton data, DAG matrices, and a specialized CNN architecture enhances both accuracy and computational efficiency.