Decoding lip language using triboelectric sensors with deep learning

doi:10.1038/s41467-022-29083-0

CORC > 自动化研究所 > 中国科学院自动化研究所 > 模式识别国家重点实验室 > 模式分析与学习团队

	Decoding lip language using triboelectric sensors with deep learning
	Lu, Yijia 1; Tian, Han 1; Cheng, Jia 1; Zhu, Fei 4; Liu, Bin 1; Wei, Shanshan 1; Ji, Linhong 1; Wang, Zhong Lin 2,3,5
刊名	NATURE COMMUNICATIONS
	2022-03-17
卷号	13 期号:1 页码:12
DOI	10.1038/s41467-022-29083-0
通讯作者	Wang, Zhong Lin(zhong.wang@mse.gatech.edu)
英文摘要	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. Lip language is an effective method of voice-off communication in daily life for people with vocal cord lesions and laryngeal and lingual injuries without occupying the hands. Collection and interpretation of lip language is challenging. Here, we propose the concept of a novel lip-language decoding system with self-powered, low-cost, contact and flexible triboelectric sensors and a well-trained dilated recurrent neural network model based on prototype learning. The structural principle and electrical properties of the flexible sensors are measured and analysed. Lip motions for selected vowels, words, phrases, silent speech and voice speech are collected and compared. The prototype learning model reaches a test accuracy of 94.5% in training 20 classes with 100 samples each. The applications, such as identity recognition to unlock a gate, directional control of a toy car and lip-motion to speech conversion, work well and demonstrate great feasibility and potential. Our work presents a promising way to help people lacking a voice live a convenient life with barrier-free communication and boost their happiness, enriches the diversity of lip-language translation systems and will have potential value in many applications.
资助项目	National Natural Science Foundation of China[51975318] ; National Key Research and Development Program of China[2018YFF0300606]
WOS关键词	NANOGENERATORS ; RECOGNITION
WOS研究方向	Science & Technology - Other Topics
语种	英语
出版者	NATURE PORTFOLIO
WOS记录号	WOS:000770426200024
资助机构	National Natural Science Foundation of China ; National Key Research and Development Program of China
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/48096]
专题	自动化研究所_模式识别国家重点实验室_模式分析与学习团队
通讯作者	Wang, Zhong Lin
作者单位	1.Tsinghua Univ, Dept Mech Engn, State Key Lab Tribol, Beijing 100084, Peoples R China 2.Univ Chinese Acad Sci, Sch Nanosci & Technol, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Beijing Inst Nanoenergy & Nanosyst, Beijing 101400, Peoples R China 4.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 5.Georgia Inst Technol, Sch Mat Sci & Engn, Atlanta, GA 30332 USA
推荐引用方式 GB/T 7714	Lu, Yijia,Tian, Han,Cheng, Jia,et al. Decoding lip language using triboelectric sensors with deep learning[J]. NATURE COMMUNICATIONS,2022,13(1):12.
APA	Lu, Yijia.,Tian, Han.,Cheng, Jia.,Zhu, Fei.,Liu, Bin.,...&Wang, Zhong Lin.(2022).Decoding lip language using triboelectric sensors with deep learning.NATURE COMMUNICATIONS,13(1),12.
MLA	Lu, Yijia,et al."Decoding lip language using triboelectric sensors with deep learning".NATURE COMMUNICATIONS 13.1(2022):12.