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
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Abstract	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, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm 2 ) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject’s mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system’s reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%). 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.
AbstractList	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, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm 2 ) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject’s mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system’s reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%). 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, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm2) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject's mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system's reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%).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, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm2) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject's mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system's reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%). 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. 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, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm2) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject’s mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system’s reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%).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. 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, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm2) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject’s mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system’s reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%). 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. 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, including signal-to-noise ratio and interelectrode interference. Hence, here, we present a novel SSI by utilizing crystalline-silicon-based strain sensors combined with a 3D convolutional deep learning algorithm. Two perpendicularly placed strain gauges with minimized cell dimension (<0.1 mm 2 ) could effectively capture the biaxial strain information with high reliability. We attached four strain sensors near the subject’s mouths and collected strain data of unprecedently large wordsets (100 words), which our SSI can classify at a high accuracy rate (87.53%). Several analysis methods were demonstrated to verify the system’s reliability, as well as the performance comparison with another SSI using sEMG electrodes with the same dimension, which exhibited a relatively low accuracy rate (42.60%). 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.
ArticleNumber	5815
Author	Shin, Yejee Kim, Kiho Kim, Kyubeen Byeon, Yunsu Hong, Seokjun Kim, Gwanho Kim, Taeseong Cho, Myeongki Son, Byung Gwan Choi, Heekyeong Kim, Jihyun Lee, Jinyoung Hwang, Dosik Shin, Jongwoon Gao, Yuyan Kang, Hong-Goo Lee, Jeong Ryong Suh, Jungmin Kim, Hwayeon Cheng, Huanyu Sang, Mingyu Jun, Yohan Son, Geonhui Kim, Taemin Kang, Kyowon Yu, Ki Jun Kwon, Yoohwan Um, Seyun
Author_xml	– sequence: 1 givenname: Taemin orcidid: 0000-0002-5405-6603 surname: Kim fullname: Kim, Taemin organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 2 givenname: Yejee surname: Shin fullname: Shin, Yejee organization: Medical Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 3 givenname: Kyowon surname: Kang fullname: Kang, Kyowon organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 4 givenname: Kiho surname: Kim fullname: Kim, Kiho organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 5 givenname: Gwanho surname: Kim fullname: Kim, Gwanho organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 6 givenname: Yunsu surname: Byeon fullname: Byeon, Yunsu organization: Medical Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 7 givenname: Hwayeon surname: Kim fullname: Kim, Hwayeon organization: Digital Signal Processing & Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 8 givenname: Yuyan surname: Gao fullname: Gao, Yuyan organization: Department of Engineering Science and Mechanics, The Pennsylvania State University – sequence: 9 givenname: Jeong Ryong surname: Lee fullname: Lee, Jeong Ryong organization: Medical Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 10 givenname: Geonhui surname: Son fullname: Son, Geonhui organization: Medical Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 11 givenname: Taeseong surname: Kim fullname: Kim, Taeseong organization: Medical Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 12 givenname: Yohan surname: Jun fullname: Jun, Yohan organization: Medical Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Department of Radiology, Harvard Medical School – sequence: 13 givenname: Jihyun surname: Kim fullname: Kim, Jihyun organization: Digital Signal Processing & Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 14 givenname: Jinyoung surname: Lee fullname: Lee, Jinyoung organization: Digital Signal Processing & Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 15 givenname: Seyun surname: Um fullname: Um, Seyun organization: Digital Signal Processing & Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 16 givenname: Yoohwan surname: Kwon fullname: Kwon, Yoohwan organization: Digital Signal Processing & Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 17 givenname: Byung Gwan surname: Son fullname: Son, Byung Gwan organization: Digital Signal Processing & Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 18 givenname: Myeongki surname: Cho fullname: Cho, Myeongki organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 19 givenname: Mingyu surname: Sang fullname: Sang, Mingyu organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 20 givenname: Jongwoon surname: Shin fullname: Shin, Jongwoon organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 21 givenname: Kyubeen surname: Kim fullname: Kim, Kyubeen organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 22 givenname: Jungmin surname: Suh fullname: Suh, Jungmin organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 23 givenname: Heekyeong orcidid: 0000-0002-8753-7833 surname: Choi fullname: Choi, Heekyeong organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 24 givenname: Seokjun surname: Hong fullname: Hong, Seokjun organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 25 givenname: Huanyu orcidid: 0000-0001-6075-4208 surname: Cheng fullname: Cheng, Huanyu organization: Department of Engineering Science and Mechanics, The Pennsylvania State University – sequence: 26 givenname: Hong-Goo surname: Kang fullname: Kang, Hong-Goo email: hgkang@yonsei.ac.kr organization: Digital Signal Processing & Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University – sequence: 27 givenname: Dosik surname: Hwang fullname: Hwang, Dosik email: dosik.hwang@yonsei.ac.kr organization: Medical Artificial Intelligence Lab, School of Electrical and Electronic Engineering, Yonsei University, Department of Electrical and Electronic Engineering, YU-Korea Institute of Science and Technology (KIST) Institute, Yonsei University – sequence: 28 givenname: Ki Jun orcidid: 0000-0002-2922-2702 surname: Yu fullname: Yu, Ki Jun email: kijunyu@yonsei.ac.kr organization: Functional Bio-integrated Electronics and Energy Management Lab, School of Electrical and Electronic Engineering, Yonsei University, Department of Electrical and Electronic Engineering, YU-Korea Institute of Science and Technology (KIST) Institute, Yonsei University
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Snippet	A wearable silent speech interface (SSI) is a promising platform that enables verbal communication without vocalization. The most widely studied methodology... Designing an efficient platform that enables verbal communication without vocalization remains a challenge. Here, the authors propose a silent speech interface...
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SubjectTerms	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)
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Title	Ultrathin crystalline-silicon-based strain gauges with deep learning algorithms for silent speech interfaces
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