Decoding lip language using triboelectric sensors with deep learning

Yijia Lu; Han Tian; Jia Cheng; Fei Zhu; Bin Liu; Shanshan Wei; Linhong Ji; Zhong Lin Wang

doi:10.1038/s41467-022-29083-0

Nature Communications (Mar 2022)

Decoding lip language using triboelectric sensors with deep learning

Yijia Lu,
Han Tian,
Jia Cheng,
Fei Zhu,
Bin Liu,
Shanshan Wei,
Linhong Ji,
Zhong Lin Wang

Affiliations

Yijia Lu: State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University
Han Tian: State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University
Jia Cheng: State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University
Fei Zhu: National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences
Bin Liu: State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University
Shanshan Wei: State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University
Linhong Ji: State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University
Zhong Lin Wang: Beijing Institute of Nanoenergy and Nanosystems, Chinese Academy of Sciences

DOI: https://doi.org/10.1038/s41467-022-29083-0
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 12

Abstract

Read online

Lip-language decoding systems are a promising technology to help people lacking a voice live a convenient life with barrier-free communication. Here, authors propose a concept of such system integrating self-powered triboelectric sensors and a well-trained dilated RNN model based on prototype learning.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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