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

Tactile and Chemical Senses01:27

Tactile and Chemical Senses

745
Tactile senses encompass touch, temperature, and pain, each mediated by specific receptors. Touch receptors detect mechanical energy or pressure against the skin. Sensory fibers from these receptors enter the spinal cord and relay information to the brain stem. Here, most fibers cross over to the opposite side of the brain. The touch information then moves to the thalamus, which projects a map of the body's surface onto the somatosensory areas of the parietal lobes in the cerebral cortex.
745
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.5K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.5K
Potential Due to a Polarized Object01:29

Potential Due to a Polarized Object

759
A neutral atom consists of a positively charged nucleus surrounded by a negatively charged electron cloud. When placed in an external electric field, the external electric force pulls the electrons and nucleus apart, opposite to the intrinsic attraction between the nucleus and the electrons. The opposing forces balance each other with a slight shift between the center of masses of the nucleus and the electron cloud, resulting in a polarized atom. On the other hand, a few molecules, like water,...
759
Potential Due to a Magnetized Object01:24

Potential Due to a Magnetized Object

780
Magnetic dipoles in magnetic materials are aligned when placed under an external magnetic field. For paramagnets and ferromagnets, dipole alignment occurs in the direction of the magnetic field. However, the dipoles align opposite to the field in the case of diamagnets. This state of magnetic polarization due to the external field is called magnetization. Magnetization is defined as the dipole moment per unit volume. It plays a similar role to polarization in electrostatics.
The vector...
780
Data Reporting and Recording01:24

Data Reporting and Recording

5.4K
Reporting and recording are crucial in data documentation. The timely, thorough, and accurate documentation of facts is essential when recording patient data. Failure to record findings during an assessment or interpretation of a problem will result in loss of information and make the patient document unreliable. The reader is left with general impressions if the information is not specific. A recording is documenting data of the individual's health information in a traceable, secure, and...
5.4K
How Data are Classified: Numerical Data00:59

How Data are Classified: Numerical Data

37.2K
Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...
37.2K

You might also read

Related Articles

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

Sort by
Same author

A Scoping Review of AI-Based Approaches for Detecting Autism Traits Using Voice and Behavioral Data.

Bioengineering (Basel, Switzerland)·2025
Same author

Cognitive Remediation for Psychosis in Virtual Reality (ThinkTactic VR): Qualitative, Iterative, and User-Centered Codevelopment Study.

JMIR mental health·2025
Same author

Multimodal Material Classification Using Visual Attention.

Sensors (Basel, Switzerland)·2024
Same author

Predicting the Arousal and Valence Values of Emotional States Using Learned, Predesigned, and Deep Visual Features.

Sensors (Basel, Switzerland)·2024
Same author

Prediction of Continuous Emotional Measures through Physiological and Visual Data.

Sensors (Basel, Switzerland)·2023
Same author

Transfer of Learning from Vision to Touch: A Hybrid Deep Convolutional Neural Network for Visuo-Tactile 3D Object Recognition.

Sensors (Basel, Switzerland)·2020

Related Experiment Video

Updated: Jan 27, 2026

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
08:52

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

77.3K

An Application of Deep Learning to Tactile Data for Object Recognition under Visual Guidance.

Ghazal Rouhafzay1, Ana-Maria Cretu2

  • 1Department of Systems and Computer Engineering, Carleton University, Ottawa, ON K1S 5B6, Canada. ghazal.rouhafzay@carleton.ca.

Sensors (Basel, Switzerland)
|April 3, 2019
PubMed
Summary

This study introduces a new robotic tactile object recognition framework using visual cues to guide data collection. A hybrid Convolutional Neural Network (CNN) approach achieved 98.97% accuracy, enhancing robotic perception capabilities.

Keywords:
Convolutional Neural NetworkHaptic explorationtactile object recognitionvisual attentionvisuo-haptic interaction

More Related Videos

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback
05:43

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

Published on: May 23, 2019

5.8K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

Related Experiment Videos

Last Updated: Jan 27, 2026

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice
08:52

Novel Object Recognition Test for the Investigation of Learning and Memory in Mice

Published on: August 30, 2017

77.3K
Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback
05:43

Applying Incongruent Visual-Tactile Stimuli during Object Transfer with Vibro-Tactile Feedback

Published on: May 23, 2019

5.8K
Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Neuroscience

Background:

  • Human object recognition integrates tactile and kinesthetic data.
  • Humans use object contours for shape perception and recognition.
  • Robotic systems can benefit from bio-inspired tactile sensing strategies.

Purpose of the Study:

  • To propose a novel framework for robotic tactile object recognition.
  • To integrate visual information for guiding tactile data acquisition.
  • To develop and evaluate advanced classification methods for tactile data.

Main Methods:

  • A framework using visual attention points to guide tactile data acquisition from virtual objects.
  • Bimodal (cutaneous and kinesthetic) tactile data collection using a virtual Force Sensing Resistor (FSR) array.
  • Classification using Convolutional Neural Networks (CNNs) and conventional methods (SVM, k-NN) with contourlet transformation.

Main Results:

  • A hybrid CNN approach with visual attention-guided contours achieved 98.97% accuracy.
  • The CNN trained on visually guided contours outperformed other methods.
  • Adaptive sensor surface sizing improved data acquisition based on object geometry.

Conclusions:

  • The proposed framework effectively enhances robotic tactile object recognition.
  • Integrating visual attention significantly improves recognition accuracy.
  • Bio-inspired multimodal tactile sensing and advanced deep learning are promising for robotics.