RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition Research
Gesture recognition serves as a foundation for Human-Computer Interaction (HCI). Although Radio Frequency Identification (RFID) is gaining popularity due to its advantages (non-invasive, low-cost, and lightweight), most existing research has only addressed the recognition of simple sign language ges...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10802886/ |
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| author | Yajun Zhang Congcong Wang Feng Li Weiqian Yu Yuankang Wang Jingying Chen |
| author_facet | Yajun Zhang Congcong Wang Feng Li Weiqian Yu Yuankang Wang Jingying Chen |
| author_sort | Yajun Zhang |
| collection | DOAJ |
| description | Gesture recognition serves as a foundation for Human-Computer Interaction (HCI). Although Radio Frequency Identification (RFID) is gaining popularity due to its advantages (non-invasive, low-cost, and lightweight), most existing research has only addressed the recognition of simple sign language gestures or body movements. There is still a significant gap in the recognition of fine-grained gestures. In this paper, we propose RF-AIRCGR as a fine-grained hand gesture recognition system for Chinese characters. It enables information input and querying through gestures in contactless scenarios, which is of great significance for both medical and educational applications. This system has three main advantages: First, by designing a tag matrix and dual-antenna layout, it fully captures fine-grained gesture data for handwritten Chinese characters. Second, it uses a variance-based sliding window method to segment continuous gesture actions. Lastly, the phase signals of Chinese characters are innovatively transformed into feature images using the Markov Transition Field. After a series of preprocessing steps, the improved C-AlexNet model is employed for deep training and experimentation. Experimental results show that RF-AIRCGR achieves average recognition accuracies of 97.85% for new users and 97.15% for new scenarios. The accuracy and robustness of the system in recognizing Chinese character gestures have been validated. |
| format | Article |
| id | doaj-art-92ba51e26dcc4a5680b89124ca4fa754 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-92ba51e26dcc4a5680b89124ca4fa7542025-01-16T00:01:32ZengIEEEIEEE Access2169-35362024-01-011219576019577710.1109/ACCESS.2024.351787610802886RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition ResearchYajun Zhang0https://orcid.org/0000-0001-9396-3313Congcong Wang1https://orcid.org/0009-0003-3673-4065Feng Li2Weiqian Yu3https://orcid.org/0009-0002-4805-9637Yuankang Wang4https://orcid.org/0009-0006-6303-9414Jingying Chen5School of Software, Xinjiang University, Ürümqi, Xinjiang, ChinaSchool of Software, Xinjiang University, Ürümqi, Xinjiang, ChinaXinjiang Uygur Autonomous Research Institute of Measurement and Testing, Ürümqi, ChinaSchool of Software, Xinjiang University, Ürümqi, Xinjiang, ChinaSchool of Software, Xinjiang University, Ürümqi, Xinjiang, ChinaSchool of Software, Xinjiang University, Ürümqi, Xinjiang, ChinaGesture recognition serves as a foundation for Human-Computer Interaction (HCI). Although Radio Frequency Identification (RFID) is gaining popularity due to its advantages (non-invasive, low-cost, and lightweight), most existing research has only addressed the recognition of simple sign language gestures or body movements. There is still a significant gap in the recognition of fine-grained gestures. In this paper, we propose RF-AIRCGR as a fine-grained hand gesture recognition system for Chinese characters. It enables information input and querying through gestures in contactless scenarios, which is of great significance for both medical and educational applications. This system has three main advantages: First, by designing a tag matrix and dual-antenna layout, it fully captures fine-grained gesture data for handwritten Chinese characters. Second, it uses a variance-based sliding window method to segment continuous gesture actions. Lastly, the phase signals of Chinese characters are innovatively transformed into feature images using the Markov Transition Field. After a series of preprocessing steps, the improved C-AlexNet model is employed for deep training and experimentation. Experimental results show that RF-AIRCGR achieves average recognition accuracies of 97.85% for new users and 97.15% for new scenarios. The accuracy and robustness of the system in recognizing Chinese character gestures have been validated.https://ieeexplore.ieee.org/document/10802886/Gesture recognitionRFIDMarkov transition fieldfine-grained recognitionneural network |
| spellingShingle | Yajun Zhang Congcong Wang Feng Li Weiqian Yu Yuankang Wang Jingying Chen RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition Research IEEE Access Gesture recognition RFID Markov transition field fine-grained recognition neural network |
| title | RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition Research |
| title_full | RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition Research |
| title_fullStr | RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition Research |
| title_full_unstemmed | RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition Research |
| title_short | RF-AIRCGR: Lightweight Convolutional Neural Network-Based RFID Chinese Character Gesture Recognition Research |
| title_sort | rf aircgr lightweight convolutional neural network based rfid chinese character gesture recognition research |
| topic | Gesture recognition RFID Markov transition field fine-grained recognition neural network |
| url | https://ieeexplore.ieee.org/document/10802886/ |
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