Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography
Electrical impedance tomography (EIT) has been applied in the field of human-computer interaction due to its advantages including the fact that it is non-invasive and has both low power consumption and a low cost. Previous work has focused on static gesture recognition based on EIT. Compared with st...
| Main Authors: | , , , , , , |
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| Format: | Article |
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MDPI AG
2022-09-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/22/19/7185 |
| _version_ | 1797477068638781440 |
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| author | Xiuyan Li Jianrui Sun Qi Wang Ronghua Zhang Xiaojie Duan Yukuan Sun Jianming Wang |
| author_facet | Xiuyan Li Jianrui Sun Qi Wang Ronghua Zhang Xiaojie Duan Yukuan Sun Jianming Wang |
| author_sort | Xiuyan Li |
| collection | DOAJ |
| description | Electrical impedance tomography (EIT) has been applied in the field of human-computer interaction due to its advantages including the fact that it is non-invasive and has both low power consumption and a low cost. Previous work has focused on static gesture recognition based on EIT. Compared with static gestures, dynamic gestures are more informative and can achieve more functions in human-machine collaboration. In order to verify the feasibility of dynamic gesture recognition based on EIT, a traditional excitation drive pattern is optimized in this paper. The drive pattern of the fixed excitation electrode is tested for the first time to simplify the measurement process of the dynamic gesture. To improve the recognition accuracy of the dynamic gestures, a dual-channel feature extraction network combining a convolutional neural network (CNN) and gated recurrent unit (GRU), namely CG-SVM, is proposed. The new center distance loss is designed in order to simultaneously supervise the intra-class distance and inter-class distance. As a result, the discriminability of the confusing data is improved. With the new excitation drive pattern and classification network, the recognition accuracy of different interference data has increased by 2.7~14.2%. The new method has stronger robustness, and realizes the dynamic gesture recognition based on EIT for the first time. |
| first_indexed | 2024-03-09T21:12:27Z |
| format | Article |
| id | doaj.art-d22fb855630e456f9e86f74552c29548 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T21:12:27Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-d22fb855630e456f9e86f74552c295482023-11-23T21:44:49ZengMDPI AGSensors1424-82202022-09-012219718510.3390/s22197185Dynamic Hand Gesture Recognition Using Electrical Impedance TomographyXiuyan Li0Jianrui Sun1Qi Wang2Ronghua Zhang3Xiaojie Duan4Yukuan Sun5Jianming Wang6School of Electronic and Information Engineering, Tiangong University, Tianjin 300387, ChinaSchool of Electronic and Information Engineering, Tiangong University, Tianjin 300387, ChinaSchool of Electronic and Information Engineering, Tiangong University, Tianjin 300387, ChinaSchool of Artificial Intelligence, Tiangong University, Tianjin 300387, ChinaSchool of Electronic and Information Engineering, Tiangong University, Tianjin 300387, ChinaSchool of Computer Science and Technology, Tiangong University, Tianjin 300387, ChinaSchool of Electronic and Information Engineering, Tiangong University, Tianjin 300387, ChinaElectrical impedance tomography (EIT) has been applied in the field of human-computer interaction due to its advantages including the fact that it is non-invasive and has both low power consumption and a low cost. Previous work has focused on static gesture recognition based on EIT. Compared with static gestures, dynamic gestures are more informative and can achieve more functions in human-machine collaboration. In order to verify the feasibility of dynamic gesture recognition based on EIT, a traditional excitation drive pattern is optimized in this paper. The drive pattern of the fixed excitation electrode is tested for the first time to simplify the measurement process of the dynamic gesture. To improve the recognition accuracy of the dynamic gestures, a dual-channel feature extraction network combining a convolutional neural network (CNN) and gated recurrent unit (GRU), namely CG-SVM, is proposed. The new center distance loss is designed in order to simultaneously supervise the intra-class distance and inter-class distance. As a result, the discriminability of the confusing data is improved. With the new excitation drive pattern and classification network, the recognition accuracy of different interference data has increased by 2.7~14.2%. The new method has stronger robustness, and realizes the dynamic gesture recognition based on EIT for the first time.https://www.mdpi.com/1424-8220/22/19/7185electrical impedance tomography (EIT)dynamic gesture recognitionexcitation drive patternartificial intelligenceneural network |
| spellingShingle | Xiuyan Li Jianrui Sun Qi Wang Ronghua Zhang Xiaojie Duan Yukuan Sun Jianming Wang Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography Sensors electrical impedance tomography (EIT) dynamic gesture recognition excitation drive pattern artificial intelligence neural network |
| title | Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography |
| title_full | Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography |
| title_fullStr | Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography |
| title_full_unstemmed | Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography |
| title_short | Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography |
| title_sort | dynamic hand gesture recognition using electrical impedance tomography |
| topic | electrical impedance tomography (EIT) dynamic gesture recognition excitation drive pattern artificial intelligence neural network |
| url | https://www.mdpi.com/1424-8220/22/19/7185 |
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