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

Autor: Taemin Kim, Yejee Shin, Kyowon Kang, Kiho Kim, Gwanho Kim, Yunsu Byeon, Hwayeon Kim, Yuyan Gao, Jeong Ryong Lee, Geonhui Son, Taeseong Kim, Yohan Jun, Jihyun Kim, Jinyoung Lee, Seyun Um, Yoohwan Kwon, Byung Gwan Son, Myeongki Cho, Mingyu Sang, Jongwoon Shin, Kyubeen Kim, Jungmin Suh, Heekyeong Choi, Seokjun Hong, Huanyu Cheng, Hong-Goo Kang, Dosik Hwang, Ki Jun Yu
Jazyk: angličtina
Rok vydání: 2022
Předmět:
Zdroj: Nature Communications, Vol 13, Iss 1, Pp 1-12 (2022)
Druh dokumentu: article
ISSN: 2041-1723
DOI: 10.1038/s41467-022-33457-9
Popis: Designing an efficient platform that enables verbal communication without vocalization remains a challenge. Here, the authors propose a silent speech interface by utilizing a deep learning algorithm combined with strain sensors attached near the subject’s mouth, able to collect 100 words and classify at a high accuracy rate.
Databáze: Directory of Open Access Journals