A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition
Abstract This study addresses the growing importance of hand gesture recognition across diverse fields, such as industry, education, and healthcare, targeting the often-neglected needs of the deaf and dumb community. The primary objective is to improve communication between individuals, thereby enha...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83108-w |
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| author | Nhat-Minh Than Son-Thuy Nguyen Dang-Nguyen Huynh Thao-Nguyen Tran Nguyen-Khoa Le Huu-Xuan Mai Cao-Dang Le Tan-Thi Pham Quang-Linh Huynh Trung-Hau Nguyen |
| author_facet | Nhat-Minh Than Son-Thuy Nguyen Dang-Nguyen Huynh Thao-Nguyen Tran Nguyen-Khoa Le Huu-Xuan Mai Cao-Dang Le Tan-Thi Pham Quang-Linh Huynh Trung-Hau Nguyen |
| author_sort | Nhat-Minh Than |
| collection | DOAJ |
| description | Abstract This study addresses the growing importance of hand gesture recognition across diverse fields, such as industry, education, and healthcare, targeting the often-neglected needs of the deaf and dumb community. The primary objective is to improve communication between individuals, thereby enhancing the overall quality of life, particularly in the context of advanced healthcare. This paper presents a novel approach for real-time hand gesture recognition using bio-impedance techniques. The developed device, powered by a Raspberry Pi and connected to electrodes for impedance data acquisition, employs an impedance chip for data collection. To categorize hand gestures, Convolutional Neuron Network (CNN), XGBoost, and Random Forest were used. The model successfully recognized up to nine distinct gestures, achieving an average accuracy of 97.24% across ten subjects using a subject-dependent strategy, showcasing the efficacy of the bioimpedance-based system in hand gesture recognition. The promising results lay a foundation for future developments in nonverbal communication between humans and machines as it contributes to the advancement of technology for the benefit of individuals with hearing impairments, addressing a critical social need. |
| format | Article |
| id | doaj-art-f8ee1c8a3da948e1b372f5f43aa0ad75 |
| institution | DOAJ |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-f8ee1c8a3da948e1b372f5f43aa0ad752025-08-20T02:46:08ZengNature PortfolioScientific Reports2045-23222024-12-0114111310.1038/s41598-024-83108-wA multi-channel bioimpedance-based device for Vietnamese hand gesture recognitionNhat-Minh Than0Son-Thuy Nguyen1Dang-Nguyen Huynh2Thao-Nguyen Tran3Nguyen-Khoa Le4Huu-Xuan Mai5Cao-Dang Le6Tan-Thi Pham7Quang-Linh Huynh8Trung-Hau Nguyen9Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Department of Biomedical Engineering, Faculty of Applied Science, Ho Chi Minh City University of Technology (HCMUT)Abstract This study addresses the growing importance of hand gesture recognition across diverse fields, such as industry, education, and healthcare, targeting the often-neglected needs of the deaf and dumb community. The primary objective is to improve communication between individuals, thereby enhancing the overall quality of life, particularly in the context of advanced healthcare. This paper presents a novel approach for real-time hand gesture recognition using bio-impedance techniques. The developed device, powered by a Raspberry Pi and connected to electrodes for impedance data acquisition, employs an impedance chip for data collection. To categorize hand gestures, Convolutional Neuron Network (CNN), XGBoost, and Random Forest were used. The model successfully recognized up to nine distinct gestures, achieving an average accuracy of 97.24% across ten subjects using a subject-dependent strategy, showcasing the efficacy of the bioimpedance-based system in hand gesture recognition. The promising results lay a foundation for future developments in nonverbal communication between humans and machines as it contributes to the advancement of technology for the benefit of individuals with hearing impairments, addressing a critical social need.https://doi.org/10.1038/s41598-024-83108-wHand gesture recognitionBioimpedanceDeep learning |
| spellingShingle | Nhat-Minh Than Son-Thuy Nguyen Dang-Nguyen Huynh Thao-Nguyen Tran Nguyen-Khoa Le Huu-Xuan Mai Cao-Dang Le Tan-Thi Pham Quang-Linh Huynh Trung-Hau Nguyen A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition Scientific Reports Hand gesture recognition Bioimpedance Deep learning |
| title | A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition |
| title_full | A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition |
| title_fullStr | A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition |
| title_full_unstemmed | A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition |
| title_short | A multi-channel bioimpedance-based device for Vietnamese hand gesture recognition |
| title_sort | multi channel bioimpedance based device for vietnamese hand gesture recognition |
| topic | Hand gesture recognition Bioimpedance Deep learning |
| url | https://doi.org/10.1038/s41598-024-83108-w |
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