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

Functional Classification of Joints01:09

Functional Classification of Joints

5.9K
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...
5.9K
Structural Classification of Joints01:20

Structural Classification of Joints

6.0K
Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
A fibrous joint is where the adjacent bones are united by fibrous connective...
6.0K
Development of the Limb Synovial Joints01:07

Development of the Limb Synovial Joints

1.9K
Joints form during embryonic development in conjunction with the formation and growth of the associated bones. The embryonic tissue that gives rise to all bones, cartilage, and connective tissues of the body is called mesenchyme.
The mesenchymal stem cells differentiate into chondrocytes that form the hyaline cartilage, and later the cartilaginous model of the bone. This model further transforms into a bone. This process is known as endochondral ossification.
During development, the limbs...
1.9K
Knee Joint01:23

Knee Joint

2.7K
The knee joint is the most complicated joint in the body. It consists of three articulations– two tibiofemoral and one patellofemoral. As is characteristic of synovial joints, the knee joint has a thin articular capsule that partially surrounds this joint cavity. Additionally, several ligaments, muscles, and cartilaginous structures support the movement of the knee.
A total of seven ligaments support the knee joint. The patellar ligament, which is also attached to the quadriceps femoris...
2.7K
Joints01:26

Joints

34.3K
Joints, also called articulations or articular surfaces, are points at which ligaments or other tissues connect adjacent bones. Joints permit movement and stability, and can be classified based on their structure or function.
Structural joint classifications are based on the material that makes up the joint as well as whether or not the joint contains a space between the bones. Joints are structurally classified as fibrous, cartilaginous, or synovial.
Fibrous Joints Are Immovable
The bones of a...
34.3K
Movement Joints in Buildings01:27

Movement Joints in Buildings

222
Movement joints in buildings are essential design elements that accommodate inevitable motions caused by various factors such as temperature changes, moisture content variations, and structural deflections. These motions, if not considered in design and construction, can lead to unsightly or dangerous damage. Movement joints are incorporated in different forms to manage these stresses and allow materials to move without causing distress.
The simplest type of movement joints, working joints, are...
222

You might also read

Related Articles

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

Sort by
Same author

AI-Enabled Mapping of Structure-Hazard Relationships for Emerging Contaminants.

Environment & health (Washington, D.C.)·2026
Same author

Predicting the progression of proliferative diabetic retinopathy: Pathophysiology, imaging phenotypes, and determinants of disease persistence despite therapy.

Survey of ophthalmology·2026
Same author

Pictilisib and nutrient stress synergize to induce methuosis via PI(4,5)P<sub>2</sub>-dependent macropinocytic dysregulation in cancer cells.

Cell death & disease·2026
Same author

Digital adiabatic evolution is universally accurate.

Nature communications·2026
Same author

Exponential Lindbladian fast forwarding and exponential amplification of certain Gibbs state properties.

Reports on progress in physics. Physical Society (Great Britain)·2026
Same author

Homozygous IbGBSS1 knockouts in hexaploid sweet potato enable amylose-free starch without a yield trade-off.

Plant science : an international journal of experimental plant biology·2026
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 10, 2025

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 Review: Point Cloud-Based 3D Human Joints Estimation.

Tianxu Xu1, Dong An1, Yuetong Jia1

  • 1Institute of Modern Optics, Nankai University, Tianjin 300350, China.

Sensors (Basel, Switzerland)
|April 3, 2021
PubMed
Summary
This summary is machine-generated.

This review surveys point cloud-based human body pose estimation methods, categorizing them into template, feature, and machine learning approaches. It highlights key works, datasets, and future research challenges in this complex field.

Keywords:
action recognitioncomputer visionconvolutional neural networkdeformation modeldepth sensorgeodesic featuresglobal featureshand pose trackinghuman representationjoint estimationpoint cloudrandom forestrandom tree walkskeleton extractionskeleton tracking

More Related Videos

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.2K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.4K

Related Experiment Videos

Last Updated: Nov 10, 2025

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
Four-Dimensional CT Analysis Using Sequential 3D-3D Registration
05:05

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Published on: November 23, 2019

8.2K
Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb
08:24

Sit-to-stand-and-walk from 120% Knee Height: A Novel Approach to Assess Dynamic Postural Control Independent of Lead-limb

Published on: August 30, 2016

10.4K

Area of Science:

  • Computer Vision
  • Robotics
  • Human-Computer Interaction

Background:

  • Human body pose estimation is crucial for applications like autonomous driving and virtual reality.
  • Existing reviews often focus on depth-based methods, leaving point cloud approaches less explored.
  • Point cloud pose estimation is challenging due to data disorder and rotation invariance.

Purpose of the Study:

  • To provide a comprehensive review of recent advancements in point cloud-based human body pose estimation.
  • To categorize existing methods based on their underlying working principles.
  • To analyze significant works, datasets, and future research directions.

Main Methods:

  • Categorization of methods into template-based, feature-based, and machine learning-based approaches.
  • Detailed analysis of significant works, including their characteristics and limitations.
  • Summary and quantitative comparison of widely used datasets and representative methods.

Main Results:

  • Identification of three primary categories for point cloud-based human pose estimation.
  • Highlighting of key research contributions and their respective strengths and weaknesses.
  • Provision of a comparative overview of current methodologies and performance benchmarks.

Conclusions:

  • Point cloud-based human pose estimation remains a challenging but rapidly evolving research area.
  • Further research is needed to address inherent difficulties like point cloud disorder and rotation invariance.
  • This review offers insights into current trends and future research avenues for the field.