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Somatosensation

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.

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Related Experiment Video

Updated: Apr 26, 2026

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

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Electronic Skin: Opportunities and Challenges in Convergence with Machine Learning.

Ja Hoon Koo¹, Young Joong Lee^2,3, Hye Jin Kim^4,5

¹Department of Semiconductor Systems Engineering and Institute of Semiconductor and System IC, Sejong University, Seoul, Republic of Korea.

Annual Review of Biomedical Engineering

|July 3, 2024

Summary

This summary is machine-generated.

Soft electronic skin (e-skin) and machine learning (ML) integration enhances robotic prostheses and biomedical devices. This synergy improves control, data processing, and clinical applications for advanced healthcare solutions.

Keywords:

electronic prostheses electronic skins machine learning algorithms robotic skins skin-mounted electronics wearable electronics

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Area of Science:

Materials Science
Robotics
Biomedical Engineering
Artificial Intelligence

Background:

Soft electronic skin (e-skin) mimics human skin's functions and physical properties.
E-skin devices are crucial for advanced robotic prostheses and biomedical instrumentation.
Machine learning (ML) enhances device control and data processing in electronic systems.

Purpose of the Study:

To review recent advancements in ML-reinforced e-skin devices.
To highlight technological breakthroughs in skin-like e-skin technologies.
To explore the convergence of e-skin and ML for healthcare applications.

Main Methods:

Review of state-of-the-art e-skin technologies focusing on skin-like properties.
Introduction of ML methods for control optimization and pattern recognition.
Analysis of practical applications integrating e-skin and ML.

Main Results:

Technological breakthroughs enable e-skin devices with enhanced skin-like properties.
ML integration significantly improves control accuracy and data processing efficiency.
Converged e-skin and ML technologies show promise for robotic prostheses and biomedical instrumentation.

Conclusions:

The integration of ML with e-skin technologies is advancing robotic prostheses and biomedical instrumentation.
This interdisciplinary field shows significant potential for clinical and industrial translation.
Further research is needed to overcome challenges in clinical and industrial transition.