Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications
Human-machine interfaces for hand gesture recognition across multiple sessions and days of doffing and re-donning while maintaining acceptable recognition accuracy are still challenging. In this paper, a flexible wristband, which was integrated with a highly sensitive capacitive pressure sensing arr...
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| Format: | Article |
| Language: | English |
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IEEE
2022-01-01
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| Series: | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9919169/ |
| _version_ | 1827927046673661952 |
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| author | Tiantong Wang Yunbiao Zhao Qining Wang |
| author_facet | Tiantong Wang Yunbiao Zhao Qining Wang |
| author_sort | Tiantong Wang |
| collection | DOAJ |
| description | Human-machine interfaces for hand gesture recognition across multiple sessions and days of doffing and re-donning while maintaining acceptable recognition accuracy are still challenging. In this paper, a flexible wristband, which was integrated with a highly sensitive capacitive pressure sensing array, was used for inter-day hand gesture recognition. The performance of the entire system was further improved by utilizing a triplet network for deep feature embedding. Seven hand gestures were included into the gesture set, and inter-day experiments which lasted for five consecutive days with three sessions on each day were conducted. Five healthy subjects participated in the experiment. Between each session, the wristband was doffed, and re-donned before the next session. The triplet network achieved an average recognition accuracy of 91.98% across all the sessions of all the subjects, and yielded a higher classification result (p < 0.05) over the convolutional neural network trained with softmax-cross-entropy loss (with an average accuracy of 84.65%). Furthermore, we also found that the capacitive array size had an evident influence on the inter-day classification result. The array with the full size (thirty-two channels) achieved a higher average recognition accuracy over all the down-sampled arrays. This work demonstrated the feasibility of improving the hand gesture recognition performance over days of usage by fabricating a wearable, flexible multi-channel capacitive wristband and implementing the triplet network. |
| first_indexed | 2024-03-13T05:46:42Z |
| format | Article |
| id | doaj.art-10aa2f6cf74d4f92abb40d860877b949 |
| institution | Directory Open Access Journal |
| issn | 1558-0210 |
| language | English |
| last_indexed | 2024-03-13T05:46:42Z |
| publishDate | 2022-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
| spelling | doaj.art-10aa2f6cf74d4f92abb40d860877b9492023-06-13T20:08:57ZengIEEEIEEE Transactions on Neural Systems and Rehabilitation Engineering1558-02102022-01-01302876288510.1109/TNSRE.2022.32127059919169Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day ApplicationsTiantong Wang0https://orcid.org/0000-0002-3654-1875Yunbiao Zhao1Qining Wang2https://orcid.org/0000-0003-3484-4810Department of Advanced Manufacturing and Robotics, Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, College of Engineering, Peking University, Beijing, ChinaDepartment of Advanced Manufacturing and Robotics, Beijing Engineering Research Center of Intelligent Rehabilitation Engineering, College of Engineering, Peking University, Beijing, ChinaDepartment of Advanced Manufacturing and Robotics, College of Engineering, Institute for Artificial Intelligence, Peking University, Beijing, ChinaHuman-machine interfaces for hand gesture recognition across multiple sessions and days of doffing and re-donning while maintaining acceptable recognition accuracy are still challenging. In this paper, a flexible wristband, which was integrated with a highly sensitive capacitive pressure sensing array, was used for inter-day hand gesture recognition. The performance of the entire system was further improved by utilizing a triplet network for deep feature embedding. Seven hand gestures were included into the gesture set, and inter-day experiments which lasted for five consecutive days with three sessions on each day were conducted. Five healthy subjects participated in the experiment. Between each session, the wristband was doffed, and re-donned before the next session. The triplet network achieved an average recognition accuracy of 91.98% across all the sessions of all the subjects, and yielded a higher classification result (p < 0.05) over the convolutional neural network trained with softmax-cross-entropy loss (with an average accuracy of 84.65%). Furthermore, we also found that the capacitive array size had an evident influence on the inter-day classification result. The array with the full size (thirty-two channels) achieved a higher average recognition accuracy over all the down-sampled arrays. This work demonstrated the feasibility of improving the hand gesture recognition performance over days of usage by fabricating a wearable, flexible multi-channel capacitive wristband and implementing the triplet network.https://ieeexplore.ieee.org/document/9919169/Capacitive sensorflexible pressure sensorhand gesture recognitioninter-daymetric learningneural network |
| spellingShingle | Tiantong Wang Yunbiao Zhao Qining Wang Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications IEEE Transactions on Neural Systems and Rehabilitation Engineering Capacitive sensor flexible pressure sensor hand gesture recognition inter-day metric learning neural network |
| title | Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications |
| title_full | Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications |
| title_fullStr | Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications |
| title_full_unstemmed | Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications |
| title_short | Hand Gesture Recognition With Flexible Capacitive Wristband Using Triplet Network in Inter-Day Applications |
| title_sort | hand gesture recognition with flexible capacitive wristband using triplet network in inter day applications |
| topic | Capacitive sensor flexible pressure sensor hand gesture recognition inter-day metric learning neural network |
| url | https://ieeexplore.ieee.org/document/9919169/ |
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