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

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
Classification of Skeletal Muscle Relaxants01:28

Classification of Skeletal Muscle Relaxants

2.6K
Skeletal muscle relaxants are a group of drugs that can reduce muscle stiffness and induce temporary paralysis to relieve pain. These agents can act centrally to reduce muscle tone or spasms in painful conditions such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), or spinal injuries; they are called antispasmodics or spasmolytics.
Peripherally acting skeletal muscle relaxants interfere with the neurotransmission at the neuromuscular end plate to induce paralysis during...
2.6K
Force Classification01:22

Force Classification

1.3K
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,...
1.3K
Classification of Neurotransmitters01:30

Classification of Neurotransmitters

3.2K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
3.2K
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

56.7K
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...
56.7K
Classification of Systems-I01:26

Classification of Systems-I

240
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:
240

You might also read

Related Articles

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

Sort by
Same author

Heavy-chain immune repertoire sequencing enables language-model prediction of antigen-specific antibodies.

Research square·2026
Same author

Comparative structural analysis of protein complexes with SPICE.

Nucleic acids research·2026
Same author

Exploring the factors of premenstrual tension syndrome and their influence on academic performance among female university students in Bangladesh: A cross-sectional study.

Women's health (London, England)·2026
Same author

Fine-tuned protein language model identifies antigen-specific B cell receptors from immune repertoires.

bioRxiv : the preprint server for biology·2025
Same author

An interpretable and balanced machine learning framework for Parkinson's disease prediction using feature engineering and explainable AI.

PloS one·2025
Same author

Predicting Antibody-Antigen Interactions with Structure-Aware LLMs: Insights from SARS-CoV-2 Variants.

Journal of chemical information and modeling·2025
Same journal

Toward Cybersecurity Testing and Monitoring of IoT Ecosystems.

SN computer science·2026
Same journal

Voxel-based Deep Regression for Enhanced Body Composition Estimation from 3D Body Scans.

SN computer science·2026
Same journal

Detecting Adverse Drug Events in Social Media: A Brief Literature Review.

SN computer science·2026
Same journal

TRAM: The Telecommunications-Related AcciMap Method.

SN computer science·2026
Same journal

A Combinatorial Approach to Synthetic Data Generation for Machine Learning.

SN computer science·2026
Same journal

To Signal or Not to Signal? A Non-cooperative Game-Theoretic Approach to Discretionary Communication Between Road Users.

SN computer science·2025
See all related articles

Related Experiment Video

Updated: Aug 10, 2025

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

3.9K

YoNet: A Neural Network for Yoga Pose Classification.

Faisal Bin Ashraf1, Muhammad Usama Islam2, Md Rayhan Kabir3

  • 1Department of Computer Science and Engineering, University of California, Riverside, CA USA.

SN Computer Science
|February 14, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep learning model for accurate yoga pose recognition, achieving 94.91% accuracy. This advancement supports at-home exercise routines by enabling reliable pose identification with limited data.

Keywords:
Deep learningImage classificationNeural networkPose recognitionYoga pose

More Related Videos

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
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

9.5K

Related Experiment Videos

Last Updated: Aug 10, 2025

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

3.9K
Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
08:20

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

1.5K
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

9.5K

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Biomedical Engineering

Background:

  • Yoga is increasingly popular for health and wellness, especially with remote work trends.
  • At-home yoga practice requires reliable pose recognition systems.
  • Existing pose recognition methods face challenges with data scarcity and classification architecture.

Purpose of the Study:

  • To develop a deep learning model for identifying five distinct yoga poses using limited data.
  • To evaluate the proposed model against state-of-the-art image classification architectures.

Main Methods:

  • A novel deep learning architecture was designed to individually extract spatial and depth features from images.
  • The model was trained and tested on a dataset of five yoga poses.
  • Performance was compared against ResNet, InceptionNet, InceptionResNet, and Xception models.

Main Results:

  • The proposed model achieved 94.91% accuracy and 95.61% precision.
  • The architecture demonstrated superior performance compared to established image classification models.
  • Effective feature extraction of spatial and depth information was key to the model's success.

Conclusions:

  • The developed deep learning model offers a robust solution for yoga pose recognition, even with limited datasets.
  • This technology can facilitate guided, at-home yoga practices.
  • The model's performance highlights the potential of specialized feature extraction for image classification tasks.