The Machine-Learning-Empowered Gesture Recognition Glove
Recently, gesture recognition technology has attracted increasing attention because it provides another means of information exchange in some special occasions, especially for auditory impaired individuals. At present, the fusion of sensor signals and artificial intelligence algorithms is the mainst...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-02-01
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| Series: | Engineering Proceedings |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4591/30/1/19 |
| _version_ | 1797580336076423168 |
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| author | Jun Luo Yuze Qian Zhenyu Gao Lei Zhang Qinliang Zhuang Kun Zhang |
| author_facet | Jun Luo Yuze Qian Zhenyu Gao Lei Zhang Qinliang Zhuang Kun Zhang |
| author_sort | Jun Luo |
| collection | DOAJ |
| description | Recently, gesture recognition technology has attracted increasing attention because it provides another means of information exchange in some special occasions, especially for auditory impaired individuals. At present, the fusion of sensor signals and artificial intelligence algorithms is the mainstream trend of gesture recognition technology. Therefore, this article designs a machine-learning-empowered gesture recognition glove. We fabricate a flexible strain sensor with a sandwich structure, which has high sensitivity and good cycle stability. After the sensors are configured in the knitted gloves, the smart gloves can respond to different gestures. Additionally, according to the representation characteristics and recognition targets of sampled signal data, we explore a segmented processing method of dynamic gesture recognition based on Logit Adaboost algorithm. After classification training, the recognition accuracy of smart gloves can reach 97%. |
| first_indexed | 2024-03-10T22:48:33Z |
| format | Article |
| id | doaj.art-b5a5fcee9b784cd894329be10e87b3ce |
| institution | Directory Open Access Journal |
| issn | 2673-4591 |
| language | English |
| last_indexed | 2024-03-10T22:48:33Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Engineering Proceedings |
| spelling | doaj.art-b5a5fcee9b784cd894329be10e87b3ce2023-11-19T10:31:51ZengMDPI AGEngineering Proceedings2673-45912023-02-013011910.3390/engproc2023030019The Machine-Learning-Empowered Gesture Recognition GloveJun Luo0Yuze Qian1Zhenyu Gao2Lei Zhang3Qinliang Zhuang4Kun Zhang5Key Laboratory of Textile Science & Technology (Ministry of Education), College of Textiles, Donghua University, Shanghai 201620, ChinaKey Laboratory of Textile Science & Technology (Ministry of Education), College of Textiles, Donghua University, Shanghai 201620, ChinaEngineering Research Center of Digitized Textile & Fashion Technology (Ministry of Education), College of Information Science & Technology, Donghua University, Shanghai 201620, ChinaEngineering Research Center of Digitized Textile & Fashion Technology (Ministry of Education), College of Information Science & Technology, Donghua University, Shanghai 201620, ChinaKey Laboratory of Textile Science & Technology (Ministry of Education), College of Textiles, Donghua University, Shanghai 201620, ChinaKey Laboratory of Textile Science & Technology (Ministry of Education), College of Textiles, Donghua University, Shanghai 201620, ChinaRecently, gesture recognition technology has attracted increasing attention because it provides another means of information exchange in some special occasions, especially for auditory impaired individuals. At present, the fusion of sensor signals and artificial intelligence algorithms is the mainstream trend of gesture recognition technology. Therefore, this article designs a machine-learning-empowered gesture recognition glove. We fabricate a flexible strain sensor with a sandwich structure, which has high sensitivity and good cycle stability. After the sensors are configured in the knitted gloves, the smart gloves can respond to different gestures. Additionally, according to the representation characteristics and recognition targets of sampled signal data, we explore a segmented processing method of dynamic gesture recognition based on Logit Adaboost algorithm. After classification training, the recognition accuracy of smart gloves can reach 97%.https://www.mdpi.com/2673-4591/30/1/19gesture recognitionflexible sensormachine learningLogit Adaboost algorithm |
| spellingShingle | Jun Luo Yuze Qian Zhenyu Gao Lei Zhang Qinliang Zhuang Kun Zhang The Machine-Learning-Empowered Gesture Recognition Glove Engineering Proceedings gesture recognition flexible sensor machine learning Logit Adaboost algorithm |
| title | The Machine-Learning-Empowered Gesture Recognition Glove |
| title_full | The Machine-Learning-Empowered Gesture Recognition Glove |
| title_fullStr | The Machine-Learning-Empowered Gesture Recognition Glove |
| title_full_unstemmed | The Machine-Learning-Empowered Gesture Recognition Glove |
| title_short | The Machine-Learning-Empowered Gesture Recognition Glove |
| title_sort | machine learning empowered gesture recognition glove |
| topic | gesture recognition flexible sensor machine learning Logit Adaboost algorithm |
| url | https://www.mdpi.com/2673-4591/30/1/19 |
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