An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements

Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Chao Wei [verfasserIn] Wansheng Lin [verfasserIn] Shaofeng Liang [verfasserIn] Mengjiao Chen [verfasserIn] Yuanjin Zheng [verfasserIn] Xinqin Liao [verfasserIn] Zhong Chen [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction

Übergeordnetes Werk:	In: Nano-Micro Letters - SpringerOpen, 2009, 14(2022), 1, Seite 18
Übergeordnetes Werk:	volume:14 ; year:2022 ; number:1 ; pages:18

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc Journal toc

DOI / URN:	10.1007/s40820-022-00875-9

Katalog-ID:	DOAJ021546851

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allfields	10.1007/s40820-022-00875-9 doi (DE-627)DOAJ021546851 (DE-599)DOAJ37524f8012c84a08bb493cbeb94617cd DE-627 ger DE-627 rakwb eng Chao Wei verfasserin aut An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects. Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction Technology T Wansheng Lin verfasserin aut Shaofeng Liang verfasserin aut Mengjiao Chen verfasserin aut Yuanjin Zheng verfasserin aut Xinqin Liao verfasserin aut Zhong Chen verfasserin aut In Nano-Micro Letters SpringerOpen, 2009 14(2022), 1, Seite 18 (DE-627)680319581 (DE-600)2642093-4 21505551 nnns volume:14 year:2022 number:1 pages:18 https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/article/37524f8012c84a08bb493cbeb94617cd kostenfrei https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/toc/2311-6706 Journal toc kostenfrei https://doaj.org/toc/2150-5551 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 1 18
spelling	10.1007/s40820-022-00875-9 doi (DE-627)DOAJ021546851 (DE-599)DOAJ37524f8012c84a08bb493cbeb94617cd DE-627 ger DE-627 rakwb eng Chao Wei verfasserin aut An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects. Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction Technology T Wansheng Lin verfasserin aut Shaofeng Liang verfasserin aut Mengjiao Chen verfasserin aut Yuanjin Zheng verfasserin aut Xinqin Liao verfasserin aut Zhong Chen verfasserin aut In Nano-Micro Letters SpringerOpen, 2009 14(2022), 1, Seite 18 (DE-627)680319581 (DE-600)2642093-4 21505551 nnns volume:14 year:2022 number:1 pages:18 https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/article/37524f8012c84a08bb493cbeb94617cd kostenfrei https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/toc/2311-6706 Journal toc kostenfrei https://doaj.org/toc/2150-5551 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 1 18
allfields_unstemmed	10.1007/s40820-022-00875-9 doi (DE-627)DOAJ021546851 (DE-599)DOAJ37524f8012c84a08bb493cbeb94617cd DE-627 ger DE-627 rakwb eng Chao Wei verfasserin aut An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects. Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction Technology T Wansheng Lin verfasserin aut Shaofeng Liang verfasserin aut Mengjiao Chen verfasserin aut Yuanjin Zheng verfasserin aut Xinqin Liao verfasserin aut Zhong Chen verfasserin aut In Nano-Micro Letters SpringerOpen, 2009 14(2022), 1, Seite 18 (DE-627)680319581 (DE-600)2642093-4 21505551 nnns volume:14 year:2022 number:1 pages:18 https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/article/37524f8012c84a08bb493cbeb94617cd kostenfrei https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/toc/2311-6706 Journal toc kostenfrei https://doaj.org/toc/2150-5551 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 1 18
allfieldsGer	10.1007/s40820-022-00875-9 doi (DE-627)DOAJ021546851 (DE-599)DOAJ37524f8012c84a08bb493cbeb94617cd DE-627 ger DE-627 rakwb eng Chao Wei verfasserin aut An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects. Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction Technology T Wansheng Lin verfasserin aut Shaofeng Liang verfasserin aut Mengjiao Chen verfasserin aut Yuanjin Zheng verfasserin aut Xinqin Liao verfasserin aut Zhong Chen verfasserin aut In Nano-Micro Letters SpringerOpen, 2009 14(2022), 1, Seite 18 (DE-627)680319581 (DE-600)2642093-4 21505551 nnns volume:14 year:2022 number:1 pages:18 https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/article/37524f8012c84a08bb493cbeb94617cd kostenfrei https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/toc/2311-6706 Journal toc kostenfrei https://doaj.org/toc/2150-5551 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 1 18
allfieldsSound	10.1007/s40820-022-00875-9 doi (DE-627)DOAJ021546851 (DE-599)DOAJ37524f8012c84a08bb493cbeb94617cd DE-627 ger DE-627 rakwb eng Chao Wei verfasserin aut An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects. Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction Technology T Wansheng Lin verfasserin aut Shaofeng Liang verfasserin aut Mengjiao Chen verfasserin aut Yuanjin Zheng verfasserin aut Xinqin Liao verfasserin aut Zhong Chen verfasserin aut In Nano-Micro Letters SpringerOpen, 2009 14(2022), 1, Seite 18 (DE-627)680319581 (DE-600)2642093-4 21505551 nnns volume:14 year:2022 number:1 pages:18 https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/article/37524f8012c84a08bb493cbeb94617cd kostenfrei https://doi.org/10.1007/s40820-022-00875-9 kostenfrei https://doaj.org/toc/2311-6706 Journal toc kostenfrei https://doaj.org/toc/2150-5551 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2005 GBV_ILN_2009 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2027 GBV_ILN_2055 GBV_ILN_2111 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2022 1 18
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author	Chao Wei
spellingShingle	Chao Wei misc Multifunctional touch sensor misc Carbon functional material misc Paper-based device misc Gradient resistance element misc Human–machine interaction misc Technology misc T An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements
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topic_title	An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction
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title	An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements
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title_full	An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements
author_sort	Chao Wei
journal	Nano-Micro Letters
journalStr	Nano-Micro Letters
lang_code	eng
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publishDateSort	2022
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container_start_page	18
author_browse	Chao Wei Wansheng Lin Shaofeng Liang Mengjiao Chen Yuanjin Zheng Xinqin Liao Zhong Chen
container_volume	14
format_se	Elektronische Aufsätze
author-letter	Chao Wei
doi_str_mv	10.1007/s40820-022-00875-9
author2-role	verfasserin
title_sort	all-in-one multifunctional touch sensor with carbon-based gradient resistance elements
title_auth	An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements
abstract	Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.
abstractGer	Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.
abstract_unstemmed	Highlights Carbon-based gradient resistance element structure is proposed for the construction of multifunctional touch sensor, which will promote wide detection and recognition range of multiple mechanical stimulations. Multifunctional touch sensor with gradient resistance element and two electrodes is demonstrated to eliminate signals crosstalk and prevent interference during position sensing for human–machine interactions. Biological sensing interface based on a deep-learning-assisted all-in-one multipoint touch sensor enables users to efficiently interact with virtual world. Abstract Human–machine interactions using deep-learning methods are important in the research of virtual reality, augmented reality, and metaverse. Such research remains challenging as current interactive sensing interfaces for single-point or multipoint touch input are trapped by massive crossover electrodes, signal crosstalk, propagation delay, and demanding configuration requirements. Here, an all-in-one multipoint touch sensor (AIOM touch sensor) with only two electrodes is reported. The AIOM touch sensor is efficiently constructed by gradient resistance elements, which can highly adapt to diverse application-dependent configurations. Combined with deep learning method, the AIOM touch sensor can be utilized to recognize, learn, and memorize human–machine interactions. A biometric verification system is built based on the AIOM touch sensor, which achieves a high identification accuracy of over 98% and offers a promising hybrid cyber security against password leaking. Diversiform human–machine interactions, including freely playing piano music and programmatically controlling a drone, demonstrate the high stability, rapid response time, and excellent spatiotemporally dynamic resolution of the AIOM touch sensor, which will promote significant development of interactive sensing interfaces between fingertips and virtual objects.
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container_issue	1
title_short	An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements
url	https://doi.org/10.1007/s40820-022-00875-9 https://doaj.org/article/37524f8012c84a08bb493cbeb94617cd https://doaj.org/toc/2311-6706 https://doaj.org/toc/2150-5551
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author2	Wansheng Lin Shaofeng Liang Mengjiao Chen Yuanjin Zheng Xinqin Liao Zhong Chen
author2Str	Wansheng Lin Shaofeng Liang Mengjiao Chen Yuanjin Zheng Xinqin Liao Zhong Chen
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doi_str	10.1007/s40820-022-00875-9
up_date	2024-07-03T21:30:46.134Z
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