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Conformable Wearable Electrodes: From Fabrication to Electrophysiological Assessment

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A Skin-Like Transparent, Low-Hysteresis, and Highly Conductive Ionogel for Human Motion Monitoring and Deep-Learning

Fu Fan^1,2, Lei Chen^1,3, Yihan Ma⁴

¹College of Mechanical and Vehicle Engineering, Hunan University, Changsha, 410082, P. R. China.

|July 21, 2025

Summary

Researchers developed a novel skin-like ionogel, offering high conductivity and low hysteresis for advanced wearable electronics. This material enhances human motion monitoring and human-machine interfaces (HMIs) through deep learning.

Keywords:

deep‐learning human motion monitor human‐machine interface ionogel low hysteresis ultra‐softness

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An Optimized O9-1/Hydrogel System for Studying Mechanical Signals in Neural Crest Cells

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Published on: August 13, 2021

Area of Science:

Materials Science
Biomedical Engineering
Wearable Technology

Background:

Human skin acts as a dynamic interface, necessitating advanced materials for on-skin electronics.
Current skin-attachable conductive materials face challenges in achieving both low modulus and low hysteresis.
Wearable sensors are crucial for health monitoring and human-machine interfaces (HMIs).

Purpose of the Study:

To introduce a novel skin-like ionogel with superior transparency, conductivity, and mechanical properties.
To demonstrate the ionogel's efficacy in human motion monitoring and biopotential detection.
To integrate the ionogel into a deep-learning-assisted HMI for handwriting recognition.

Main Methods:

Fabrication of a transparent, low-hysteresis ionogel.
Characterization of the ionogel's conductivity, modulus, transparency, and adhesion.
Development and implementation of a 1D-ResNet algorithm for handwriting recognition.
Integration of ionogel-based sensors for motion and biopotential monitoring.

Main Results:

The ionogel exhibited a low modulus (5.08 kPa), high transparency (>92%), low hysteresis (<3%), and conductivity (0.86 S/m).
Ionogel-based sensors showed high sensitivity for human motion monitoring and biopotential detection.
Handwriting recognition using the 1D-ResNet algorithm achieved 98.13% accuracy.

Conclusions:

The developed ionogel offers a promising solution for flexible and stretchable wearable electronics.
This material has significant potential for advanced healthcare monitoring and human-machine interfaces.
The combination of low modulus and low hysteresis in ionogels opens new avenues for next-generation wearable devices.