Multifunctional, Self-Adhesive MXene-Based Hydrogel Flexible Strain Sensors for Hand-Written Digit Recognition with Assistance of Deep Learning

Abstract

The conductive hydrogel as a flexible sensor not only has certain mechanical flexibility but also can be used in the field of human health detection and human−computer interaction. Herein, by introduction of tannic acid (TA) with MXene into the polyacrylamide (PAM)/carboxymethyl chitosan (CMC) doublenetwork hydrogel, a hydrogel with high stretchability, self-adhesion, and high sensitivity was prepared. CMC and PAM form a semiinterpenetrating double-network of high toughness and durability through electrostatic interactions and multiple hydrogen bonding networks. The abundant hydrophilic functional groups on TA and MXene form multiple hydrogen bonds simultaneously with the polymer network, ensuring high stretchability and sensitivity of the hydrogel. The hydrogel can display an accurate response to a variety of stimulus signals and can monitor both human joint movements and small physiological signal changes. It can also be combined with deep learning algorithms to classify handwritten digits with an accuracy rate of 98%. This work can promote the application of hydrogel sensors with durability and high sensitivity. The combination of algorithms and flexible sensors provides important ideas for the further development of flexible devices.

Recent Advances in Machine Learning Assisted Hydrogel Flexible Sensing

“…ML-assisted handwriting recognition is critical to the development of document processing, human-computer interaction, next-generation biometrics, and information communication security. [47,117,[133][134] Figure 6. A) Bidirectional long short-term memory (BiLSTM) model assisted handwriting recognition feature process.…”