返回
Supramolecular Wood-Based Human–Computer Interaction Interface for Text Recognition
DOI:10.1021/acsami.5c19469.png)
摘要
En 中文
文本识别是人机交互(HCI)的基础,但传统的各向同性传感界面在解析手写复杂方向特征方面存在固有局限,尤其是汉字笔画方向。本研究利用天然木材固有的各向异性结构,开发了一种机械性能强、高可拉伸且敏感的基于水凝胶的界面,能够准确区分多样化的手写轨迹。由此制备的用于人机交互(HCI)的木材基软水凝胶(WSH-HCI)沿纤维轴的拉伸强度达到25 MPa─比横向方向高6.5倍─同时保持稳定的多方向信号识别能力,并在汉字基本笔画识别中达到96%的准确率。这些性能源于保存的取向纤维素纳米纤维通道和强PVA-纤维素氢键网络。演示包括英文字母和汉字的实时识别。本研究为高性能各向异性水凝胶传感器在先进手写识别和智能人机交互中的应用提供了可行途径。
Keyword:
Cellulose
Hydrogels
Sensors
Thermodynamic properties
Wood
wood-based hydrogel
human−computer interaction
text recognition
anisotropic sensing
high-performance sensors
期刊
A
IF:
0
论文数:
1.6K
被引数:
0
机构
引用论文
Alphabet Handwriting Recognition: From Wood-Framed Hydrogel Arrays Design to Machine Learning Decoding字母手写识别: 从木框水凝胶阵列设计到机器学习解码
ADVANCED SCIENCE
IF14.1
Preparation and Characterization of Softwood and Hardwood Nanofibril Hydrogels: Toward Wound Dressing Applications软木与硬木纳米纤丝水凝胶的制备与表征:面向伤口敷料应用
BIOMACROMOLECULES
IF5.4
Machine-Learning Assisted Handwriting Recognition Using Graphene Oxide-Based Hydrogel基于氧化石墨烯水凝胶的机器学习辅助手写识别
In Situ Polymerized MXene/Polypyrrole/Hydroxyethyl Cellulose- Based Flexible Strain Sensor Enabled by Machine Learning for Handwriting Recognition基于原位聚合MXene/聚吡咯/羟乙基纤维素的柔性应变传感器,通过机器学习实现手写识别
Self-Adhesive, Anti-Freezing MXene-Based Hydrogel Strain Sensor for Motion Monitoring and Handwriting Recognition with Deep Learning自粘,防冻基于MXene的水凝胶应变传感器,用于运动监测和带有深度学习的手写识别
Humidity Adaptive Antifreeze Hydrogel Sensor for Intelligent Control and Human-Computer Interaction用于智能控制和人机交互的湿度自适应防冻水凝胶传感器
SMALL
IF12.1
Rapidly self-healing, highly conductive, stretchable, body-attachable hydrogel sensor for soft electronics用于软电子产品的快速自修复,高导电性,可拉伸,可附着于人体的水凝胶传感器

