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

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
Classification of Signals01:30

Classification of Signals

630
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
630
Relative Motion Analysis using Rotating Axes01:25

Relative Motion Analysis using Rotating Axes

505
Consider a component AB undergoing a linear motion. Along with a linear motion, point B also rotates around point A. To comprehend this complex movement, position vectors for both points A and B are established using a stationary reference frame.
However, to express the relative position of point B relative to point A, an additional frame of reference, denoted as x'y', is necessary. This additional frame not only translates but also rotates relative to the fixed frame, making it...
505
Classification of Systems-I01:26

Classification of Systems-I

243
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:
243
Absolute Motion Analysis- General Plane Motion01:24

Absolute Motion Analysis- General Plane Motion

252
Visualize a drone, with its propellers spinning rapidly, hovering mid-air. The fascinating movements and operations of this drone can be comprehended by applying the principle of general plane motion.
As the drone's propellers rotate, an upward force is generated that counteracts the force of gravity, enabling the drone to lift off from the ground. This initial movement of the drone is along a straight path, representing a form of translational motion. In this phase, every point on the...
252
Relative Motion Analysis using Rotating Axes-Problem Solving01:29

Relative Motion Analysis using Rotating Axes-Problem Solving

435
Consider a crane whose telescopic boom rotates with an angular velocity of 0.04 rad/s and angular acceleration of 0.02 rad/s2. Along with the rotation, the boom also extends linearly with a uniform speed of 5 m/s. The extension of the boom is measured at point D, which is measured with respect to the fixed point C on the other end of the boom. For the given instant, the distance between points C and D is 60 meters.
Here, in order to determine the magnitude of velocity and acceleration for point...
435

You might also read

Related Articles

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

Sort by
Same author

Multi-Entropy Feature Concatenation for Data-Efficient Cross-Subject Classification of Alzheimer's Disease and Frontotemporal Dementia from Single-Channel EEG.

Entropy (Basel, Switzerland)·2026
Same author

Early identification of abnormal pulmonary infectious diseases using unsupervised anomaly detection.

Quantitative imaging in medicine and surgery·2025
Same author

A Novel Multi-Scale Entropy Approach for EEG-Based Lie Detection with Channel Selection.

Entropy (Basel, Switzerland)·2025
Same author

EEG-ERnet: Emotion Recognition based on Rhythmic EEG Convolutional Neural Network Model.

Journal of integrative neuroscience·2025
Same author

Effective Motor Skill Learning Induces Inverted-U Load-Dependent Activation in Contralateral Pre-Motor and Supplementary Motor Area.

Human brain mapping·2025
Same author

Arsenic mobility and microbial community composition in the sediments of coastal wetlands driven by tidal action.

Journal of environmental sciences (China)·2025

Related Experiment Video

Updated: Aug 14, 2025

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis
05:51

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

Published on: October 12, 2011

11.1K

Nystagmus patterns classification framework based on deep learning and optical flow.

Sheng Kong1, Zheming Huang2, Weike Deng1

  • 1School of Computer Science and Technology, Guangdong University of Technology, China.

Computers in Biology and Medicine
|January 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a deep learning method to automatically recognize benign paroxysmal positional vertigo (BPPV) nystagmus patterns from infrared videos. The new approach significantly improves the accuracy of classifying nystagmus types, aiding clinical diagnosis.

Keywords:
Benign paroxysmal positional vertigoDeep learningIris segmentationNystagmus classificationOptical flow

More Related Videos

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
07:26

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

7.9K
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

675

Related Experiment Videos

Last Updated: Aug 14, 2025

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis
05:51

VisioTracker, an Innovative Automated Approach to Oculomotor Analysis

Published on: October 12, 2011

11.1K
Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking
07:26

Characterizing the Relationship Between Eye Movement Parameters and Cognitive Functions in Non-demented Parkinson's Disease Patients with Eye Tracking

Published on: September 26, 2019

7.9K
Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing
06:25

Author Spotlight: Assessment of Visual Acuity in Central Vision Loss Through Motion-Based Peripheral Vision Testing

Published on: February 23, 2024

675

Area of Science:

  • Neurology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Benign paroxysmal positional vertigo (BPPV) is a common cause of peripheral vertigo.
  • Accurate identification of nystagmus patterns in infrared videos is crucial for BPPV diagnosis.
  • Existing methods struggle with recognizing diverse nystagmus patterns, particularly torsional nystagmus.

Purpose of the Study:

  • To develop an automated method for recognizing BPPV nystagmus patterns using deep learning and optical flow.
  • To enhance the accuracy and efficiency of classifying nystagmus patterns, assisting clinicians in BPPV diagnosis.

Main Methods:

  • Adaptive preprocessing for invalid video frames (e.g., blinks) and efficient iris-pupil segmentation.
  • Deep learning-based optical flow for extracting nystagmus information.
  • A Nystagmus Video Classification Network (NVCN) using ConvNeXt for feature extraction and LSTM for temporal analysis.

Main Results:

  • The NVCN model achieved 94.91% accuracy and 93.70% F1 score for general nystagmus pattern classification.
  • The model demonstrated high performance in recognizing torsional nystagmus, with 97.75% accuracy and 97.48% F1 score.
  • Experimental results confirm the framework's effectiveness in distinguishing various nystagmus patterns.

Conclusions:

  • The proposed deep learning framework effectively automates the recognition of BPPV nystagmus patterns.
  • This technology can serve as a valuable tool for clinicians in diagnosing and classifying BPPV subtypes.
  • The method shows particular promise for improving the detection of challenging torsional nystagmus.