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

You might also read

Related Articles

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

Sort by
Same author

Reinforcement Learning-Based Optimization of Ku-Band Low-Noise Amplifier.

Micromachines·2026
Same author

A 45.6 <i>m</i> NaCTFSI/NaFSI hybrid electrolyte for high-voltage aqueous sodium-ion batteries operable at subzero temperatures.

Science advances·2026
Same author

Resolving energy transfer dynamics in Eu²⁺-activated multi-site phosphors via metaheuristic optimization and physics-informed neural networks.

Nature communications·2026
Same author

Discovering Multi-Compositional Li-Argyrodite Solid-State Electrolytes via Experimental Active Learning.

Small (Weinheim an der Bergstrasse, Germany)·2024
Same author

Dual-Modal Sensing Skin Adaptive to Daylight, Darkness, and Ultraviolet Light for Simultaneous Full-Field Deformation Measurement and Mechanoluminescence Responses.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2024
Same author

Enhancing P2/O3 Biphasic Cathode Performance for Sodium-Ion Batteries: A Metaheuristic Approach to Multi-Element Doping Design.

Small (Weinheim an der Bergstrasse, Germany)·2024

Related Experiment Video

Updated: Feb 23, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.3K

An extremely simple macroscale electronic skin realized by deep machine learning.

Kee-Sun Sohn1, Jiyong Chung2, Min-Young Cho3

  • 1Faculty of Nanotechnology and Advanced Materials Engineering, Sejong University, Seoul, 143-747, Republic of Korea. kssohn@sejong.ac.kr.

Scientific Reports
|September 13, 2017
PubMed
Summary
This summary is machine-generated.

Researchers developed a simple, single-layered electronic skin (e-skin) using a piezoresistive composite film. Deep machine learning processes resistance changes to instantly evaluate pressure level and location, enabling advanced sensory applications.

More Related Videos

Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management
08:50

Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management

Published on: September 2, 2015

9.5K
Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

5.1K

Related Experiment Videos

Last Updated: Feb 23, 2026

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.3K
Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management
08:50

Fabrication and Characterization of a Conformal Skin-like Electronic System for Quantitative, Cutaneous Wound Management

Published on: September 2, 2015

9.5K
Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment
10:03

Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

Published on: July 22, 2022

5.1K

Area of Science:

  • Materials Science
  • Artificial Intelligence
  • Sensory Technology

Background:

  • Macroscale electronic skin (e-skin) typically requires complex multi-layered structures and patterned components for signal readout.
  • Existing e-skin technologies face challenges in achieving high resolution and simplicity in design.

Purpose of the Study:

  • To develop a simplified macroscale e-skin using a single-layered material.
  • To leverage deep machine learning for pressure sensing capabilities in a non-patterned material.
  • To demonstrate the potential of this approach for diverse high-end applications.

Main Methods:

  • Fabrication of a single-layered piezoresistive composite film using multi-walled carbon nanotube (MWCNT) and polydimethylsiloxane (PDMS).
  • Implementation of a deep neural network (DNN) to analyze electrical resistance changes induced by applied pressure.
  • Development of algorithms for instantaneous evaluation of pressure level and spatial location.

Main Results:

  • Successful realization of a macroscale e-skin without any nano-, micro-, or macro-patterns.
  • DNN accurately processed resistance variations to determine pressure magnitude and precise location.
  • Demonstrated the material's capability to function as a smart sensory device.

Conclusions:

  • A simplified, single-layered e-skin is achievable using a piezoresistive composite and deep learning.
  • This approach overcomes the complexity of traditional patterned e-skin designs.
  • The technology holds significant promise for applications including touch panels, wearable devices, infrastructure monitoring, and medical diagnostics.