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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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 immobile...
Force Classification01:22

Force Classification

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,...
Structural Classification of Joints01:20

Structural Classification of Joints

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...
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

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Related Experiment Video

Updated: Jun 1, 2026

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Gait-based gender classification using mixed conditional random field.

Maodi Hu1, Yunhong Wang, Zhaoxiang Zhang

  • 1State Key Laboratory of Virtual Reality Technology and Systems, Beihang University, Beijing, China. Londeehu@gmail.com

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|May 31, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a novel mixed conditional random field (MCRF) model for gender classification using human gait. The MCRF model outperforms traditional methods by integrating shape and temporal dynamics for more accurate gait recognition.

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Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
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Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

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Last Updated: Jun 1, 2026

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder
06:54

Clinical-oriented Three-dimensional Gait Analysis Method for Evaluating Gait Disorder

Published on: March 4, 2018

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits
06:25

Paw-Print Analysis of Contrast-Enhanced Recordings (PrAnCER): A Low-Cost, Open-Access Automated Gait Analysis System for Assessing Motor Deficits

Published on: August 12, 2019

Area of Science:

  • Biometrics
  • Computer Vision
  • Machine Learning

Background:

  • Gait-based gender classification is a challenging biometric identification task.
  • Traditional temporal modeling methods often struggle to capture complex gait variations.

Purpose of the Study:

  • To propose a supervised modeling approach for gait-based gender classification.
  • To develop a sequential model integrating spatial and temporal gait features.

Main Methods:

  • A mixed conditional random field (MCRF) model was developed, integrating gender labels for competitive learning.
  • Spatial features were extracted using pyramids of fitting coefficients for gait shape descriptors.
  • Temporal features were derived from clustered neighborhood-preserving embeddings for stance indexes.
  • Feature sparseness was enhanced using partition and Xie-Beni indices.

Main Results:

  • The MCRF model demonstrated superior performance compared to Hidden Markov Models (HMMs) and individually trained CRFs.
  • Experiments were conducted on CASIA (Data set B) and IRIP Gait Databases.
  • The proposed method effectively fuses shape descriptors and stance indexes for improved classification.

Conclusions:

  • The MCRF model offers a robust framework for gait-based gender classification.
  • Integrating spatial and temporal gait dynamics leads to enhanced classification accuracy.
  • The MCRF approach shows significant potential for biometric applications.