WEKO3
アイテム
Dynamic Hand Gesture Recognition by Hand Landmark Classification Using Long Short-Term Memory
http://hdl.handle.net/10228/0002001807
http://hdl.handle.net/10228/0002001807c8665d18-396c-411b-a749-8cff9d3e1ec7
| 名前 / ファイル | ライセンス | アクション |
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10461888.pdf (1.2 MB)
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| アイテムタイプ | 共通アイテムタイプ(1) | |||||||||||||
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| 公開日 | 2025-07-29 | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Dynamic Hand Gesture Recognition by Hand Landmark Classification Using Long Short-Term Memory | |||||||||||||
| 言語 | en | |||||||||||||
| 著者 |
Khawaritzmi Abdallah Ahmad,
× Khawaritzmi Abdallah Ahmad,
× Higashi, Takahiro
× 吉田, 香
WEKO
27669
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| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) Universiti Putra Malaysia Press | |||||||||||||
| 言語 | en | |||||||||||||
| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Hand gestures are a valuable modality for human-computer interaction, conveying information that can be used as input. Dynamic hand gestures, prevalent in real-world scenarios, necessitate considering temporal factors such as gesture initiation, termination, and frame sequence. A Long Short-Term Memory (LSTM) based recognition model was proposed to address this challenge. Data availability for dynamic hand gesture research is a significant hurdle. The dataset introduced by Fronteddu et al. provides 27 classes of dynamic hand gestures, serving as a suitable training resource. MediaPipe Hands, a computer vision framework, was leveraged to extract keypoints from each frame, capturing spatial features fed into the LSTM model. Experiments were conducted to determine the optimal dropout rate for the LSTM model. Results indicated that a dropout rate of 70% yielded the highest accuracy, achieving up to 98.53% validation accuracy and 99.71% test accuracy. These findings demonstrate the effectiveness of the proposed LSTM-based recognition model for dynamic hand gestures. Future research could explore integrating other deep learning techniques, such as attention mechanisms, to enhance the accuracy and robustness of dynamic hand gesture recognition systems. Additionally, investigating the application of the proposed model in real-world scenarios, such as virtual and augmented reality, would be valuable in assessing its practical utility. | |||||||||||||
| 言語 | en | |||||||||||||
| 書誌情報 |
en : Pertanika Journal of Science & Technology 巻 33, 号 S2, p. 73-84, 発行日 2025-02-25 |
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| 出版社 | ||||||||||||||
| 出版者 | Universiti Putra Malaysia Press | |||||||||||||
| 言語 | en | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | Classification | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | dynamic hand gesture | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | human-computer interaction | |||||||||||||
| キーワード | ||||||||||||||
| 言語 | en | |||||||||||||
| 主題Scheme | Other | |||||||||||||
| 主題 | long short-term memory | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | VoR | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||||||||||
| DOI | ||||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.47836/pjst.33.S2.05 | |||||||||||||
| ISSN | ||||||||||||||
| 収録物識別子タイプ | PISSN | |||||||||||||
| 収録物識別子 | 0128-7680 | |||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2231-8526 | |||||||||||||
| 研究者情報 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/190_ja.html | |||||||||||||
| 論文ID（連携） | ||||||||||||||
| 値 | 10461888 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 14731 | |||||||||||||
