Yan Guihua, Hu Xichen, Miao Ziyue, Liu Yongde, Zeng Xianhai, Lin Lu, Ikkala Olli, Peng Bo
College of Environmental Engineering, Henan University of Technology, Zhengzhou, 450001, China.
Department of Applied Physics, Aalto University, Aalto, FI-00076, Finland.
Adv Sci (Weinh). 2024 Dec;11(47):e2404437. doi: 10.1002/advs.202404437. Epub 2024 Nov 4.
Handwriting recognition is a highly integrated system, demanding hardware to collect handwriting signals and software to deal with input data. Nonetheless, the design of such a system from scratch with sustainable materials and an easily accessible computing network presents significant challenges. In pursuit of this goal, a flexible, and electrically conductive wood-derived hydrogel array is developed as a handwriting input panel, enabling recognizing alphabet handwriting assisted by machine learning technique. For this, lignin extraction-refill, polypyrrole coating, and polyacrylic acid filling, endowing flexibility, and electrical conduction to wood are sequentially implemented. Subsequently, these woods are manufactured into a 5 × 5 array, creating a matrix of signals upon handwriting. Efficient handwritten recognition is then achieved through appropriate manual feature extraction and algorithms with low complexity within a computing network, as demonstrated in this work, the strategic choice of expertise-based feature engineering and simplified algorithms effectively boost the overall model performance on handwriting recognition. With potential adaptability, further applications in customized wearable devices and hands-on healthcare appliances are envisioned.
手写识别是一个高度集成的系统，既需要硬件来收集手写信号，也需要软件来处理输入数据。然而，用可持续材料和易于访问的计算网络从头设计这样一个系统面临重大挑战。为了实现这一目标，一种柔性且导电的木质衍生水凝胶阵列被开发为手写输入面板，借助机器学习技术能够识别字母手写。为此，依次实施木质素提取 - 再填充、聚吡咯涂层和聚丙烯酸填充，赋予木材柔韧性和导电性。随后，将这些木材制成5×5阵列，在手写时创建信号矩阵。然后通过在计算网络内进行适当的手动特征提取和低复杂度算法实现高效的手写识别，如本工作所示，基于专业知识的特征工程和简化算法的策略性选择有效地提升了手写识别的整体模型性能。鉴于其潜在的适应性，设想了在定制可穿戴设备和实际操作的医疗保健器具中的进一步应用。
