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Decoding lip language using triboelectric sensors with deep learning

文献类型：期刊论文

作者Lu, Yijia5; Tian, Han5; Cheng, Jia5; Zhu, Fei4; Liu, Bin5; Wei, Shanshan5; Ji, Linhong5; Wang, Zhong Lin1,2,3
刊名NATURE COMMUNICATIONS
出版日期2022-03-17
卷号13期号:1页码:12
DOI10.1038/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.
WOS关键词NANOGENERATORS ; RECOGNITION
资助项目National Natural Science Foundation of China[51975318] ; National Key Research and Development Program of China[2018YFF0300606]
WOS研究方向Science & Technology - Other Topics
语种英语
WOS记录号WOS:000770426200024
出版者NATURE PORTFOLIO
资助机构National Natural Science Foundation of China ; National Key Research and Development Program of China
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.Georgia Inst Technol, Sch Mat Sci & Engn, Atlanta, GA 30332 USA
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.Tsinghua Univ, Dept Mech Engn, State Key Lab Tribol, Beijing 100084, Peoples R China
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.

入库方式： OAI收割

来源：自动化研究所

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