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

Somatosensation01:33

Somatosensation

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.
Design Example: Resistive Touchscreen01:14

Design Example: Resistive Touchscreen

A device engineer plays a crucial role in designing user interfaces for mobile devices. One such interface is the resistive touchscreen, which fundamentally consists of two metallic layers: a flexible upper layer and a rigid lower layer, separated by a narrow gap. The high resistance between these two layers is a key characteristic of this design.
When a user touches the screen, the two layers make contact at a specific point known as the touchpoint. This contact reduces the resistance between...
Sensory Functions of the Skin01:16

Sensory Functions of the Skin

The skin is the largest organ of the human body and plays a crucial role in our sensory perception. It contains a vast network of sensory receptors that contribute to the skin's protective function by perceiving physical, biological, and environmental cues and generating relevant responses.
There are two main categories of receptors on the skin: capsulated and non-capsulated. The non-capsulated ones are mainly the pain receptors. The capsulated ones can be further categorized based on the...

You might also read

Related Articles

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

Sort by
Same author

Predictive model for wetness sensation in fabrics based on machine learning and SHAP.

International journal of biometeorology·2026
Same author

Effect of fabric-skin frictional force and temperature on surface roughness and wetness perception.

Perception·2025
Same author

Effect of Fiber Type, Water Content, and Velocity on Wetness Perception by the Volar Forearm Test: Threshold Detection Test.

Perception·2020
Same author

Effect of Fiber Type, Water Content, and Velocity on Wetness Perception by the Volar Forearm Test: Stimulus Intensity Test.

Perception·2019
Same author

Tumor size and lymph node metastasis are prognostic markers of small cell lung cancer in a Chinese population.

Medicine·2018
Same author

Gold nanocatalysts supported on carbon for electrocatalytic oxidation of organic molecules including guanines in DNA.

Dalton transactions (Cambridge, England : 2003)·2018
Same journal

High-resolution kitsch by AI: Why society needs art, not more AI content.

Perception·2026
Same journal

Benchmarking spatial discrimination thresholds of two-frame motion defined forms compared to luminance and stereoscopic defined forms.

Perception·2026
Same journal

The effect of face masks on the perception of trustworthiness and competence in individuals with autistic traits.

Perception·2026
Same journal

The importance of external features for categorizing ethnicity: can Koreans identify Korean, Japanese, and Chinese faces?

Perception·2026
Same journal

Interoception, alexithymia, and motor congruency: Psychological drivers of body ownership in virtual reality.

Perception·2026
Same journal

The frustration of a small <i>n</i>.

Perception·2026
See all related articles

Related Experiment Video

Updated: Jul 3, 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

Predictive models and parameter analysis for multiple tactile perceptions in skin-wet fabrics interface.

Zhaohua Zhang1, Meiping Guo1, Yue Yang1

  • 1College of Fashion and Design, Donghua University, China.

Perception
|July 2, 2026
PubMed
Summary
This summary is machine-generated.

Fabric wetness significantly impacts tactile perception and comfort. This study links fabric properties and physical interactions to subjective feelings, using machine learning to predict overall hand feel.

Keywords:
SHAP methodpredictive modelskin–wet fabrics interfacetactile perceptiontotal hand feeling

Related Experiment Videos

Last Updated: Jul 3, 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

Area of Science:

  • Textile science and materials engineering
  • Human-computer interaction
  • Biophysics of skin-fabric interactions

Background:

  • Understanding skin-wet fabric interaction is crucial for textile comfort.
  • Current knowledge on the mechanisms driving tactile perception of wet fabrics is limited.

Purpose of the Study:

  • To investigate the relationship between fabric properties, physical parameters, and subjective tactile perception of wet fabrics.
  • To develop predictive models for total hand feeling value based on objective measurements.
  • To identify key factors influencing the tactile experience of wet textiles.

Main Methods:

  • Characterized 20 fabrics by properties like water content and density.
  • Measured real-time tactile physical parameters (cooling rate, pressure, friction, acceleration) during fingertip interaction.
  • Collected subjective ratings for wetness, coldness, roughness, stiffness, and total hand feeling.
  • Employed machine learning (Gradient Boosting, XGBoost, Random Forest, ANNR, PLSR) for predictive modeling.

Main Results:

  • Fabric water content significantly influenced both tactile perceptions and physical parameters.
  • Artificial Neural Networks Regression (ANNR) model showed the best performance for predicting total hand feeling value (R²=0.727).
  • SHapley Additive exPlanations identified fabric water content and mechanical stimulation as key factors affecting hand feel.

Conclusions:

  • Objective fabric properties and physical measurements can predict subjective tactile perception of wetness.
  • Findings offer insights for designing textiles with enhanced comfort and optimized tactile properties.
  • This research provides a foundation for the tactile design of smart textiles and wearable products.