# Advancements in Machine Learning-Assisted Flexible Electronics: Technologies, Applications, and Future Prospects

## TL;DR

This paper reviews how machine learning improves flexible electronics for smart sensing, covering technologies, applications, and future directions.

## Contribution

The paper systematically analyzes ML algorithms' roles in signal processing, defect detection, and applications in flexible electronics.

## Key findings

- ML algorithms like LSTM and CNN enhance signal analysis accuracy in flexible electronics.
- Reinforcement learning optimizes manufacturing processes and compensates for noise interference.
- Applications include wearable health monitoring and intelligent control of soft robots.

## Abstract

The integration of flexible electronics and machine learning (ML) algorithms has become a revolutionary force driving the field of intelligent sensing, giving rise to a new generation of intelligent devices and systems. This article provides a systematic review of core technologies and practical applications of ML in flexible electronics. It focuses on analyzing the theoretical frameworks of algorithms such as the Long Short-Term Memory Network (LSTM), Convolutional Neural Network (CNN), and Reinforcement Learning (RL) in the intelligent processing of sensor signals (IPSS), multimodal feature extraction (MFE), process defect and anomaly detection (PDAD), and data compression and edge computing (DCEC). This study explores the performance advantages of these technologies in optimizing signal analysis accuracy, compensating for interference in high-noise environments, optimizing manufacturing process parameters, etc., and empirically analyzes their potential applications in wearable health monitoring systems, intelligent control of soft robots, performance optimization of self-powered devices, and intelligent perception of epidermal electronic systems.