Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation
Gesture recognition plays a significant role in human-machine interaction (HMI) system. This paper proposes a gesture-controlled reconfigurable metasurface system based on surface electromyography (sEMG) for real-time beam deflection and polarization conversion. By recognizing the sEMG signals of us...
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| Format: | Article |
| Language: | English |
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De Gruyter
2025-01-01
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| Series: | Nanophotonics |
| Subjects: | |
| Online Access: | https://doi.org/10.1515/nanoph-2024-0572 |
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| _version_ | 1832570438154190848 |
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| author | Chen Junzai Li Weiran Gong Kailuo Lu Xiaojie Tong Mei Song Wang Xiaoyi Yang Guo-Min |
| author_facet | Chen Junzai Li Weiran Gong Kailuo Lu Xiaojie Tong Mei Song Wang Xiaoyi Yang Guo-Min |
| author_sort | Chen Junzai |
| collection | DOAJ |
| description | Gesture recognition plays a significant role in human-machine interaction (HMI) system. This paper proposes a gesture-controlled reconfigurable metasurface system based on surface electromyography (sEMG) for real-time beam deflection and polarization conversion. By recognizing the sEMG signals of user gestures through a pre-trained convolutional neural network (CNN) model, the system dynamically modulates the metasurface, enabling precise control of the deflection direction and polarization state of electromagnetic waves. Experimental results demonstrate that the proposed system achieves high-precision electromagnetic wave manipulation, in response to different gestures. This system has significant potential applications in intelligent device control, virtual reality systems, and wireless communication technology, and is expected to contribute to the advancement and innovation of HMI technology by integration of more advanced metasurfaces and sEMG processing technologies. |
| format | Article |
| id | doaj-art-776f3d85a6d845759669741f6c61d676 |
| institution | Kabale University |
| issn | 2192-8614 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | De Gruyter |
| record_format | Article |
| series | Nanophotonics |
| spelling | doaj-art-776f3d85a6d845759669741f6c61d6762025-02-02T15:46:12ZengDe GruyterNanophotonics2192-86142025-01-0114110711910.1515/nanoph-2024-0572Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulationChen Junzai0Li Weiran1Gong Kailuo2Lu Xiaojie3Tong Mei Song4Wang Xiaoyi5Yang Guo-Min6College of Electronic and Information Engineering, Tongji University, Shanghai200092, ChinaKey Laboratory for Information Science of Electromagnetic Waves, School of Information Science and Technology, Fudan University, Shanghai200433, ChinaCollege of Electronic and Information Engineering, Tongji University, Shanghai200092, ChinaCollege of Electronic and Information Engineering, Tongji University, Shanghai200092, ChinaCollege of Electronic and Information Engineering, Tongji University, Shanghai200092, ChinaCollege of Electronic and Information Engineering, Shanghai Institute of Intelligent Science and Technology, Tongji University, Shanghai200092, ChinaKey Laboratory for Information Science of Electromagnetic Waves, School of Information Science and Technology, Fudan University, Shanghai200433, ChinaGesture recognition plays a significant role in human-machine interaction (HMI) system. This paper proposes a gesture-controlled reconfigurable metasurface system based on surface electromyography (sEMG) for real-time beam deflection and polarization conversion. By recognizing the sEMG signals of user gestures through a pre-trained convolutional neural network (CNN) model, the system dynamically modulates the metasurface, enabling precise control of the deflection direction and polarization state of electromagnetic waves. Experimental results demonstrate that the proposed system achieves high-precision electromagnetic wave manipulation, in response to different gestures. This system has significant potential applications in intelligent device control, virtual reality systems, and wireless communication technology, and is expected to contribute to the advancement and innovation of HMI technology by integration of more advanced metasurfaces and sEMG processing technologies.https://doi.org/10.1515/nanoph-2024-0572metasurfacesurface electromyographyconvolutional neural networkgesture recognition |
| spellingShingle | Chen Junzai Li Weiran Gong Kailuo Lu Xiaojie Tong Mei Song Wang Xiaoyi Yang Guo-Min Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation Nanophotonics metasurface surface electromyography convolutional neural network gesture recognition |
| title | Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation |
| title_full | Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation |
| title_fullStr | Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation |
| title_full_unstemmed | Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation |
| title_short | Gesture-controlled reconfigurable metasurface system based on surface electromyography for real-time electromagnetic wave manipulation |
| title_sort | gesture controlled reconfigurable metasurface system based on surface electromyography for real time electromagnetic wave manipulation |
| topic | metasurface surface electromyography convolutional neural network gesture recognition |
| url | https://doi.org/10.1515/nanoph-2024-0572 |
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