Jove
Visualize
Contact Us

Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

4.3K
Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
An...
4.3K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

137
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
137
Association Areas of the Cortex01:21

Association Areas of the Cortex

5.7K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.7K
Force Classification01:22

Force Classification

1.4K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.4K

You might also read

Related Articles

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

Sort by
Same author

Integrating Convolutional and Recurrent Neural Networks for Enhanced Medical Image Captioning.

Advances in experimental medicine and biology·2025
Same author

Enhanced Brain Tumor Classification with Convolutional Neural Networks.

Advances in experimental medicine and biology·2025
Same author

Skeleton Reconstruction Using Generative Adversarial Networks for Human Activity Recognition Under Occlusion.

Sensors (Basel, Switzerland)·2025
Same author

Leveraging Artificial Occluded Samples for Data Augmentation in Human Activity Recognition.

Sensors (Basel, Switzerland)·2025
Same author

Augmented reality in intensive care nursing education: A scoping review.

Nurse education in practice·2025
Same author

A Multi-Modal Egocentric Activity Recognition Approach towards Video Domain Generalization.

Sensors (Basel, Switzerland)·2024
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 Experiment Video

Updated: Aug 16, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K

A Multimodal Fusion Approach for Human Activity Recognition.

Dimitrios Koutrintzes1, Evaggelos Spyrou2, Eirini Mathe3

  • 1Institute of Informatics and Telecommunications, National Center for Scientific Research - "Demokritos", Athens, Greece.

International Journal of Neural Systems
|December 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a multimodal approach for human activity recognition (HAR) using 3D visual data. The method fuses features from multiple 2D representations for improved video-based HAR performance.

Keywords:
Human activity recognitiondeep convolutional neural networksmultimodal fusion

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
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

Related Experiment Videos

Last Updated: Aug 16, 2025

Design and Analysis for Fall Detection System Simplification
08:05

Design and Analysis for Fall Detection System Simplification

Published on: April 6, 2020

10.8K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
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

Area of Science:

  • Computer Vision
  • Machine Learning
  • Artificial Intelligence

Background:

  • Human Activity Recognition (HAR) is crucial for various applications, requiring analysis of human motion and behavior from sensor data.
  • Existing methods often rely on single data modalities, limiting comprehensive understanding of complex activities.

Purpose of the Study:

  • To propose a novel multimodal approach for video-based Human Activity Recognition (HAR).
  • To leverage 3D visual data (RGB + depth) for enhanced HAR by creating diverse 2D representations.

Main Methods:

  • Utilized 3D skeletal sequences and RGB data from an RGB+depth camera.
  • Transformed data into six distinct 2D image representations, including spectral and dynamic images.
  • Employed six Convolutional Neural Networks (CNNs) for feature extraction, followed by feature fusion and Support Vector Machine (SVM) classification.

Main Results:

  • The proposed multimodal approach demonstrated superior performance in human activity recognition tasks.
  • Experiments included single-view, cross-view, and cross-subject evaluations on a challenging dataset.
  • Outperformed three other state-of-the-art methods in most experimental settings.

Conclusions:

  • The multimodal fusion of diverse 2D representations derived from 3D data significantly enhances video-based HAR.
  • The approach offers a robust and effective solution for recognizing human activities in complex scenarios.
  • This work contributes to advancing the field of HAR through innovative data representation and fusion techniques.