Electronic Skin: Opportunities and Challenges in Convergence with Machine Learning

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.