Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces

A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology for SSI focuses on surface electromyography (sEMG). However, sEMG suffers from low scalability because of signal quality-related issues, includ...

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Veröffentlicht in:	Nature communications Jg. 13; H. 1; S. 5815 - 12
Hauptverfasser:	Kim, Taemin, Shin, Yejee, Kang, Kyowon, Kim, Kiho, Kim, Gwanho, Byeon, Yunsu, Kim, Hwayeon, Gao, Yuyan, Lee, Jeong Ryong, Son, Geonhui, Kim, Taeseong, Jun, Yohan, Kim, Jihyun, Lee, Jinyoung, Um, Seyun, Kwon, Yoohwan, Son, Byung Gwan, Cho, Myeongki, Sang, Mingyu, Shin, Jongwoon, Kim, Kyubeen, Suh, Jungmin, Choi, Heekyeong, Hong, Seokjun, Cheng, Huanyu, Kang, Hong-Goo, Hwang, Dosik, Yu, Ki Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 03.10.2022 Nature Publishing Group Nature Portfolio
Schlagworte:	142/126 639/166/985 639/166/987 639/925/350/59 639/925/927/59 Accuracy Algorithms Classification Crystal structure Crystallinity Deep learning Electromyography Gauges Humanities and Social Sciences Interfaces Machine learning multidisciplinary Reliability analysis Science Science (multidisciplinary) Sensors Signal quality Signal to noise ratio Silicon Speech Strain gauges Verbal communication Vocalization behavior Words (language)
ISSN:	2041-1723, 2041-1723
Online-Zugang:	Volltext
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