Citation

Liu J, Wang L, Xu R, Zhang X, Zhao J, Liu H, Chen F, Qu L, Tian M. ACS Nano 2024; ePub(ePub): ePub.

Copyright

(Copyright © 2024, American Chemical Society)

DOI

10.1021/acsnano.3c13221

PMID

38597459

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.

Language: en

Keywords

flexible tactile sensor; gesture recognition; machine learning; starfish-inspired micropillar; underwater communications