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

1.2K
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.
1.2K
Somatosensation01:33

Somatosensation

45.1K
The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
45.1K

You might also read

Related Articles

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

Sort by
Same author

Impact of cationic, anionic, and amphiphilic emulsifiers on the physicochemical properties and applications of xanthan gum-shea butter bigels.

International journal of biological macromolecules·2026
Same author

Atrial repolarization wave: a new approach using spline-based feature engineering and explainable AI for atrial arrhythmia diagnosis.

Biomedizinische Technik. Biomedical engineering·2026
Same author

Modeling Astrocyte-Driven Repair of Visuomotor Deficits in Alzheimer's Thalamic Circuitry.

IEEE transactions on computational biology and bioinformatics·2026
Same author

Deciphering atrial repolarization morphology: A spline interpolation framework for atrial arrhythmia diagnosis.

Technology and health care : official journal of the European Society for Engineering and Medicine·2026
Same author

Corrigendum to "Structuro-optical optimization of ethyl cellulose-modified candelilla wax/canola oil oleogels for use as sustainable fat replacers in steamed bread systems" [Int. J. Biol. Macromol. 330 (Part 1) (November 2025) 147902].

International journal of biological macromolecules·2026
Same author

Plasmonic Grating on Monolayer MoS<sub>2</sub> for Strong Photoluminescence Enhancement and Ultrasensitive Surface-Enhanced Raman Scattering (SERS) Detection.

Nano letters·2025

Related Experiment Video

Updated: Mar 23, 2026

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects
07:32

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Published on: September 1, 2016

13.3K

Texture- and deformability-based surface recognition by tactile image analysis.

Anwesha Khasnobish1, Monalisa Pal2, D N Tibarewala3

  • 1School of Bioscience and Engineering, Jadavpur University, Raja S.C. Mullick Road, Kolkata, West Bengal, 700032, India. anweshakhasno@gmail.com.

Medical & Biological Engineering & Computing
|March 24, 2016
PubMed
Summary
This summary is machine-generated.

This study developed a tactile sensing system for robots to identify surfaces by their deformability and texture. The system achieved high accuracy in recognizing household surfaces, daily-use textures, and biomembranes, enhancing robotic manipulation capabilities.

Keywords:
Linear discriminant analysisSupport vector machineSurface recognitionTactile imagek-Nearest neighbour

More Related Videos

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy
12:26

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

6.6K
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

2.3K

Related Experiment Videos

Last Updated: Mar 23, 2026

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects
07:32

Measurement of Vibration Detection Threshold and Tactile Spatial Acuity in Human Subjects

Published on: September 1, 2016

13.3K
Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy
12:26

Control of Cell Adhesion using Hydrogel Patterning Techniques for Applications in Traction Force Microscopy

Published on: January 29, 2022

6.6K
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

2.3K

Area of Science:

  • Robotics
  • Artificial Intelligence
  • Materials Science

Background:

  • Tactile sensing is crucial for object recognition and manipulation in robotics.
  • Artificial arms require tactile feedback for human-like interaction and control.
  • Surface properties like deformability and texture are key identifiers.

Purpose of the Study:

  • To develop an effective technique for identifying surfaces based on deformability and texture using tactile data analysis.
  • To enable artificial arms with tactile sensors to recognize various materials.
  • To apply the developed method for recognizing biomembranes.

Main Methods:

  • Acquired tactile data from human and robot hand explorations.
  • Pre-processed tactile images and extracted relevant features.
  • Utilized Support Vector Machine (SVM), Linear Discriminant Analysis, and k-Nearest Neighbour (kNN) classifiers for recognition.

Main Results:

  • Successfully recognized six household surfaces by deformability and five daily-use surfaces by texture.
  • Achieved 83% accuracy in recognizing surface deformability using Linear SVM.
  • kNN classifier recognized textures with 89% accuracy, and SVM with RBF kernel recognized biomembranes with 78% accuracy.

Conclusions:

  • The developed tactile analysis technique effectively recognizes materials based on deformability and texture.
  • Classifiers demonstrated good generalization on unseen data, proving robust for material recognition.
  • This research advances tactile sensing for improved robotic manipulation and human-computer interfaces.