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 electrode...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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SpringerOpen
2022-06-01
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| Series: | Nano-Micro Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s40820-022-00875-9 |
| _version_ | 1811243828913373184 |
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| author | Chao Wei Wansheng Lin Shaofeng Liang Mengjiao Chen Yuanjin Zheng Xinqin Liao Zhong Chen |
| author_facet | Chao Wei Wansheng Lin Shaofeng Liang Mengjiao Chen Yuanjin Zheng Xinqin Liao Zhong Chen |
| author_sort | Chao Wei |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T14:13:59Z |
| format | Article |
| id | doaj.art-37524f8012c84a08bb493cbeb94617cd |
| institution | Directory Open Access Journal |
| issn | 2311-6706 2150-5551 |
| language | English |
| last_indexed | 2024-04-12T14:13:59Z |
| publishDate | 2022-06-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Nano-Micro Letters |
| spelling | doaj.art-37524f8012c84a08bb493cbeb94617cd2022-12-22T03:29:46ZengSpringerOpenNano-Micro Letters2311-67062150-55512022-06-0114111810.1007/s40820-022-00875-9An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance ElementsChao Wei0Wansheng Lin1Shaofeng Liang2Mengjiao Chen3Yuanjin Zheng4Xinqin Liao5Zhong Chen6Department of Electronic Science, Xiamen UniversityDepartment of Electronic Science, Xiamen UniversityDepartment of Electronic Science, Xiamen UniversityDepartment of Electronic Science, Xiamen UniversitySchool of Electrical and Electronic Engineering, Nanyang Technological UniversityDepartment of Electronic Science, Xiamen UniversityDepartment of Electronic Science, Xiamen UniversityHighlights 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.https://doi.org/10.1007/s40820-022-00875-9Multifunctional touch sensorCarbon functional materialPaper-based deviceGradient resistance elementHuman–machine interaction |
| spellingShingle | Chao Wei Wansheng Lin Shaofeng Liang Mengjiao Chen Yuanjin Zheng Xinqin Liao Zhong Chen An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements Nano-Micro Letters Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction |
| title | An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements |
| title_full | An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements |
| title_fullStr | An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements |
| title_full_unstemmed | An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements |
| title_short | An All-In-One Multifunctional Touch Sensor with Carbon-Based Gradient Resistance Elements |
| title_sort | all in one multifunctional touch sensor with carbon based gradient resistance elements |
| topic | Multifunctional touch sensor Carbon functional material Paper-based device Gradient resistance element Human–machine interaction |
| url | https://doi.org/10.1007/s40820-022-00875-9 |
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