A Rapid Adaptation Approach for Dynamic Air‑Writing Recognition Using Wearable Wristbands with Self‑Supervised Contrastive Learning  

作　　者：Yunjian Guo Kunpeng Li Wei Yue Nam‑Young Kim Yang Li Guozhen Shen Jong‑Chul Lee 

机构地区：[1]Department of Electronic Convergence Engineering,Kwangwoon University,Seoul 01897,South Korea [2]Radio Frequency Integrated Circuit(RFIC)Bio Centre,Kwangwoon University,Seoul 01897,South Korea [3]Department of Electronic Engineering,Kwangwoon University,Seoul 01897,South Korea [4]School of Microelectronics,Shandong University,Jinan 250101,People’s Republic of China [5]State Key Laboratory of Integrated Chips and Systems,Fudan University,Shanghai 200433,People’s Republic of China [6]School of Integrated Circuits and Electronics,Beijing Institute of Technology,Beijing 100081,People’s Republic of China

出　　处：《Nano-Micro Letters》2025年第2期417-431,共15页纳微快报（英文版）

基　　金：supported by the Research Grant Fund from Kwangwoon University in 2023,the National Natural Science Foundation of China under Grant(62311540155);the Taishan Scholars Project Special Funds(tsqn202312035);the open research foundation of State Key Laboratory of Integrated Chips and Systems.

摘　　要：Wearable wristband systems leverage deep learning to revolutionize hand gesture recognition in daily activities.Unlike existing approaches that often focus on static gestures and require extensive labeled data,the proposed wearable wristband with selfsupervised contrastive learning excels at dynamic motion tracking and adapts rapidly across multiple scenarios.It features a four-channel sensing array composed of an ionic hydrogel with hierarchical microcone structures and ultrathin flexible electrodes,resulting in high-sensitivity capacitance output.Through wireless transmission from a Wi-Fi module,the proposed algorithm learns latent features from the unlabeled signals of random wrist movements.Remarkably,only few-shot labeled data are sufficient for fine-tuning the model,enabling rapid adaptation to various tasks.The system achieves a high accuracy of 94.9%in different scenarios,including the prediction of eight-direction commands,and air-writing of all numbers and letters.The proposed method facilitates smooth transitions between multiple tasks without the need for modifying the structure or undergoing extensive task-specific training.Its utility has been further extended to enhance human–machine interaction over digital platforms,such as game controls,calculators,and three-language login systems,offering users a natural and intuitive way of communication.

关 键 词：Wearable wristband Self-supervised contrastive learning Dynamic gesture Air-writing Human-machine interaction 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TP18[自动化与计算机技术—计算机科学与技术]