水下
手势
计算机科学
手势识别
灵敏度(控制系统)
人工智能
人机交互
工程类
电子工程
海洋学
地质学
作者
Jiaxu Liu,Lihong Wang,Ruidong Xu,Xinwei Zhang,Jian Zhao,Hong Liu,Fuxing Chen,Lijun Qu,Mingwei Tian
出处
期刊:ACS Nano
[American Chemical Society]
日期:2024-04-10
卷期号:18 (16): 10818-10828
被引量:7
标识
DOI:10.1021/acsnano.3c13221
摘要
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.
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