@article{ref1,
title="Underwater gesture recognition meta-gloves for marine immersive communication",
journal="ACS nano",
year="2024",
author="Liu, Jiaxu and Wang, Lihong and Xu, Ruidong and Zhang, Xinwei and Zhao, Jisheng and Liu, Hong and Chen, Fuxing and Qu, Lijun and Tian, Mingwei",
volume="ePub",
number="ePub",
pages="ePub-ePub",
abstract="Rapid advancements in immersive communications and artificial intelligence have created a pressing demand for high-performance tactile sensing gloves capable of delivering high sensitivity and a wide sensing range. Unfortunately, existing tactile sensing gloves fall short in terms of user comfort and are ill-suited for underwater applications. To address these limitations, we propose a flexible hand gesture recognition glove (GRG) that contains high-performance micropillar tactile sensors (MPTSs) inspired by the flexible tube foot of a starfish. The as-prepared flexible sensors offer a wide working range (5 Pa to 450 kPa), superfast response time (23 ms), reliable repeatability (∼10000 cycles), and a low limit of detection. Furthermore, these MPTSs are waterproof, which makes them well-suited for underwater applications. By integrating the high-performance MPTSs with a machine learning algorithm, the proposed GRG system achieves intelligent recognition of 16 hand gestures under water, which significantly extends real-time and effective communication capabilities for divers. The GRG system holds tremendous potential for a wide range of applications in the field of underwater communications.<p /> <p>Language: en</p>",
language="en",
issn="1936-0851",
doi="10.1021/acsnano.3c13221",
url="http://dx.doi.org/10.1021/acsnano.3c13221"
}