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Bionic Ultra‐Sensitive Self‐Powered Electromechanical Sensor for Muscle‐Triggered Communication Application

The past few decades have witnessed the tremendous progress of human–machine interface (HMI) in communication, education, and manufacturing fields. However, due to signal acquisition devices’ limitations, the research on HMI related to communication aid applications for the disabled is progressing s...

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Autores principales: Zhou, Hong, Li, Dongxiao, He, Xianming, Hui, Xindan, Guo, Hengyu, Hu, Chenguo, Mu, Xiaojing, Wang, Zhong Lin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336610/
https://www.ncbi.nlm.nih.gov/pubmed/34081406
http://dx.doi.org/10.1002/advs.202101020
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author Zhou, Hong
Li, Dongxiao
He, Xianming
Hui, Xindan
Guo, Hengyu
Hu, Chenguo
Mu, Xiaojing
Wang, Zhong Lin
author_facet Zhou, Hong
Li, Dongxiao
He, Xianming
Hui, Xindan
Guo, Hengyu
Hu, Chenguo
Mu, Xiaojing
Wang, Zhong Lin
author_sort Zhou, Hong
collection PubMed
description The past few decades have witnessed the tremendous progress of human–machine interface (HMI) in communication, education, and manufacturing fields. However, due to signal acquisition devices’ limitations, the research on HMI related to communication aid applications for the disabled is progressing slowly. Here, inspired by frogs’ croaking behavior, a bionic triboelectric nanogenerator (TENG)‐based ultra‐sensitive self‐powered electromechanical sensor for muscle‐triggered communication HMI application is developed. The sensor possesses a high sensitivity (54.6 mV mm(−1)), a high‐intensity signal (± 700 mV), and a wide sensing range (0–5 mm). The signal intensity is 206 times higher than that of traditional biopotential electromyography methods. By leveraging machine learning algorithms and Morse code, the safe, accurate (96.3%), and stable communication aid HMI applications are achieved. The authors' bionic TENG‐based electromechanical sensor provides a valuable toolkit for HMI applications of the disabled, and it brings new insights into the interdisciplinary cross‐integration between TENG technology and bionics.
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spelling pubmed-83366102021-08-11 Bionic Ultra‐Sensitive Self‐Powered Electromechanical Sensor for Muscle‐Triggered Communication Application Zhou, Hong Li, Dongxiao He, Xianming Hui, Xindan Guo, Hengyu Hu, Chenguo Mu, Xiaojing Wang, Zhong Lin Adv Sci (Weinh) Research Articles The past few decades have witnessed the tremendous progress of human–machine interface (HMI) in communication, education, and manufacturing fields. However, due to signal acquisition devices’ limitations, the research on HMI related to communication aid applications for the disabled is progressing slowly. Here, inspired by frogs’ croaking behavior, a bionic triboelectric nanogenerator (TENG)‐based ultra‐sensitive self‐powered electromechanical sensor for muscle‐triggered communication HMI application is developed. The sensor possesses a high sensitivity (54.6 mV mm(−1)), a high‐intensity signal (± 700 mV), and a wide sensing range (0–5 mm). The signal intensity is 206 times higher than that of traditional biopotential electromyography methods. By leveraging machine learning algorithms and Morse code, the safe, accurate (96.3%), and stable communication aid HMI applications are achieved. The authors' bionic TENG‐based electromechanical sensor provides a valuable toolkit for HMI applications of the disabled, and it brings new insights into the interdisciplinary cross‐integration between TENG technology and bionics. John Wiley and Sons Inc. 2021-06-03 /pmc/articles/PMC8336610/ /pubmed/34081406 http://dx.doi.org/10.1002/advs.202101020 Text en © 2021 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Zhou, Hong
Li, Dongxiao
He, Xianming
Hui, Xindan
Guo, Hengyu
Hu, Chenguo
Mu, Xiaojing
Wang, Zhong Lin
Bionic Ultra‐Sensitive Self‐Powered Electromechanical Sensor for Muscle‐Triggered Communication Application
title Bionic Ultra‐Sensitive Self‐Powered Electromechanical Sensor for Muscle‐Triggered Communication Application
title_full Bionic Ultra‐Sensitive Self‐Powered Electromechanical Sensor for Muscle‐Triggered Communication Application
title_fullStr Bionic Ultra‐Sensitive Self‐Powered Electromechanical Sensor for Muscle‐Triggered Communication Application
title_full_unstemmed Bionic Ultra‐Sensitive Self‐Powered Electromechanical Sensor for Muscle‐Triggered Communication Application
title_short Bionic Ultra‐Sensitive Self‐Powered Electromechanical Sensor for Muscle‐Triggered Communication Application
title_sort bionic ultra‐sensitive self‐powered electromechanical sensor for muscle‐triggered communication application
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8336610/
https://www.ncbi.nlm.nih.gov/pubmed/34081406
http://dx.doi.org/10.1002/advs.202101020
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