Deep learning-based approach for gesture recognition with static hand representation
In the era of Artificial Intelligence (AI), our science and technology have reached to a lot of milestones, especially in the field of human-robot interaction (HRI). By fusing with the image processing techniques, AI-based strategy to enhance mutual recognition of HRI via hand signs is proposed in t...
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| Format: | Article |
| Language: | English |
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University of Belgrade - Faculty of Mechanical Engineering, Belgrade
2025-01-01
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| Series: | FME Transactions |
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| Online Access: | https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2025/1451-20922503458N.pdf |
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| _version_ | 1849699544440242176 |
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| author | Nguyen Song Hung Phan Duc Minh Ngo Ha Quang Thinh |
| author_facet | Nguyen Song Hung Phan Duc Minh Ngo Ha Quang Thinh |
| author_sort | Nguyen Song Hung |
| collection | DOAJ |
| description | In the era of Artificial Intelligence (AI), our science and technology have reached to a lot of milestones, especially in the field of human-robot interaction (HRI). By fusing with the image processing techniques, AI-based strategy to enhance mutual recognition of HRI via hand signs is proposed in this investigation. Primarily, robotic hardware, theoretical computation of gripper design and vision-based techniques are introduced to establish the working environment. Then, the proposed framework including controller design and interactive platform is demonstrated. Several hand signs from human operator are collected and trained. Our approach is experimented in two cases for validating the effectiveness and properness of the proposed method with varying light condition. From these results, it can be seen obviously that this scheme is applicable in different fields such as human-aware collaboration, cognitive robot or sign language translation system. |
| format | Article |
| id | doaj-art-152bb40dccf84a0eaf386e9bb66af252 |
| institution | DOAJ |
| issn | 1451-2092 2406-128X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | University of Belgrade - Faculty of Mechanical Engineering, Belgrade |
| record_format | Article |
| series | FME Transactions |
| spelling | doaj-art-152bb40dccf84a0eaf386e9bb66af2522025-08-20T03:18:33ZengUniversity of Belgrade - Faculty of Mechanical Engineering, BelgradeFME Transactions1451-20922406-128X2025-01-0153345847010.5937/fme2503458H1451-20922503458NDeep learning-based approach for gesture recognition with static hand representationNguyen Song Hung0Phan Duc Minh1Ngo Ha Quang Thinh2Ho Chi Minh City University of Technology, Faculty of Mechanical Engineering, Ho Chi Minh City, VietnamHo Chi Minh City University of Technology, Faculty of Mechanical Engineering, Ho Chi Minh City, VietnamHo Chi Minh City University of Technology, Faculty of Mechanical Engineering, Ho Chi Minh City, VietnamIn the era of Artificial Intelligence (AI), our science and technology have reached to a lot of milestones, especially in the field of human-robot interaction (HRI). By fusing with the image processing techniques, AI-based strategy to enhance mutual recognition of HRI via hand signs is proposed in this investigation. Primarily, robotic hardware, theoretical computation of gripper design and vision-based techniques are introduced to establish the working environment. Then, the proposed framework including controller design and interactive platform is demonstrated. Several hand signs from human operator are collected and trained. Our approach is experimented in two cases for validating the effectiveness and properness of the proposed method with varying light condition. From these results, it can be seen obviously that this scheme is applicable in different fields such as human-aware collaboration, cognitive robot or sign language translation system.https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2025/1451-20922503458N.pdfdeep learninghand gesture recognitionhuman-robot interactioncomputer visionmotion control |
| spellingShingle | Nguyen Song Hung Phan Duc Minh Ngo Ha Quang Thinh Deep learning-based approach for gesture recognition with static hand representation FME Transactions deep learning hand gesture recognition human-robot interaction computer vision motion control |
| title | Deep learning-based approach for gesture recognition with static hand representation |
| title_full | Deep learning-based approach for gesture recognition with static hand representation |
| title_fullStr | Deep learning-based approach for gesture recognition with static hand representation |
| title_full_unstemmed | Deep learning-based approach for gesture recognition with static hand representation |
| title_short | Deep learning-based approach for gesture recognition with static hand representation |
| title_sort | deep learning based approach for gesture recognition with static hand representation |
| topic | deep learning hand gesture recognition human-robot interaction computer vision motion control |
| url | https://scindeks-clanci.ceon.rs/data/pdf/1451-2092/2025/1451-20922503458N.pdf |
| work_keys_str_mv | AT nguyensonghung deeplearningbasedapproachforgesturerecognitionwithstatichandrepresentation AT phanducminh deeplearningbasedapproachforgesturerecognitionwithstatichandrepresentation AT ngohaquangthinh deeplearningbasedapproachforgesturerecognitionwithstatichandrepresentation |