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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 黃育熙 | zh_TW |
| dc.contributor.advisor | Yu-Hsi Huang | en |
| dc.contributor.author | 曾奇鈞 | zh_TW |
| dc.contributor.author | Chi-Chun Tseng | en |
| dc.date.accessioned | 2025-08-19T16:06:09Z | - |
| dc.date.available | 2025-08-20 | - |
| dc.date.copyright | 2025-08-19 | - |
| dc.date.issued | 2025 | - |
| dc.date.submitted | 2025-08-05 | - |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/98761 | - |
| dc.description.abstract | 本研究旨在發展一套以深度學習為基礎的手語辨識系統，並將其應用於機械手臂的人機互動控制。研究動機主要來自於兩個方向。其一是協助聾啞人士在日常生活中的溝通，使其在公共服務、醫療照護等情境中能更自如地表達需求與想法。其二則是針對環境噪音干擾較嚴重的場域（如工廠），提供一種以手勢作為機械手臂輸入指令的替代方案，不僅具備抗噪音的優勢，亦便於遠距操控，進而提升操作效率、減少人員暴露於危險區域的風險，具備高度實務價值。
本研究先透過MediaPipe 模型獲取人體骨架座標，並針對這些骨架座標進行資料增強處理，包括正規化、標準化等操作，以提升模型對手勢變化的辨識能力。接著，將處理後的資料輸入至基於空間距離分群策略的時空圖卷積網路，進行訓練，以獲得可以辨識台灣手語的系統。在應用上，我們把這個手語辨識系統當作指派機械手臂任務的翻譯器，讓使用者可以使用手語來命令機械手臂工作。基本上，這些由手語轉換的控制指令是透過機器人操作系統傳遞給機械手臂來完成自主取物的任務。為協助機械手臂進行精準的實體互動，本系統亦搭配輪廓辨識方法來執行物件的偵測與定位，以強化整體應用的實用性與靈活性。 本系統設計以模組化與即時性為考量，具備約 95% 的手語動作辨識準確率與穩定的操作反應，適用於輔助溝通介面、特殊教育訓練場域，或其他需要非語音控制的智慧環境。此外，系統使用人體骨架座標作為主要特徵輸入，相較於傳統以影像為基礎的卷積神經網路方法，不僅能顯著降低所需訓練資料量，亦在資源受限的條件下維持良好的辨識準確度，避免因影像背景、光線等外部因素造成辨識干擾，展現高效且具彈性的實作潛力。研究亦探討資料處理與模型設計對辨識效能的影響，為後續應用提供技術依據。 | zh_TW |
| dc.description.abstract | This study presents a deep learning-based sign language recognition system integrated with robotic arm control for human-robot interaction. The system is designed to support communication for individuals with hearing and speech impairments and to provide a gesture-based interface for robot control in noisy environments such as factories. Utilizing MediaPipe for human skeletal keypoint extraction, the system applies data augmentation techniques (normalization, standardization) followed by classification using a Spatial Temporal Graph Convolutional Network with the Spatial-Distance partitioning strategy (STGCN-SD). Recognized gestures are translated into control commands and executed by the robotic arm via the ROS2 framework. For object interaction, the system incorporates contour-based object detection and localization, enabling task-specific responses based on the recognized signs.
Achieving approximately 95% recognition accuracy, the system demonstrates real-time responsiveness and modular design, making it suitable for assistive communication, special education, and smart environments. Unlike traditional CNN-based methods, the use of skeletal data significantly reduces training data requirements and is less affected by background or lighting variations, enhancing efficiency and robustness. The research also explores the influence of data processing and model architecture on recognition performance, providing a foundation for future expansion in multi-modal input, sentence-level recognition, and cross-lingual sign language systems. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2025-08-19T16:06:09Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2025-08-19T16:06:09Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 誌 謝 I
摘 要 III ABSTRACT V 目 次 VII 表目次 XI 圖目次 XIII 第一章 緒論 1 1.1 研究動機 1 1.2 文獻回顧 2 1.3 論文結構 4 第二章 手語辨識系統之相關基本原理 7 2.1 台灣手語和手語辨識方法的簡介 7 2.1.1 台灣手語的簡介 7 2.1.2 手語辨識的感測式裝置 8 2.1.3 手語辨識的視覺影像 9 2.2 深度學習 9 2.2.1 多層感知器 10 2.2.2 卷積神經網路 15 2.2.3 圖形神經網路 16 2.2.4 圖形卷積神經網路 18 2.3 動作辨識 20 2.4 人體姿態估計工具 21 2.4.1 OpenPose 21 2.4.2 MediaPipe 21 2.5 時空圖卷積網路 21 2.6 基於ST-GCN的手語辨識 22 第三章 建構台灣手語辨識系統 35 3.1 資料蒐集 35 3.2 建構手語的時空圖 36 3.2.1 六種手語關鍵點樣型 36 3.2.2 骨架標準化 37 3.3 台灣手語辨識模型的實作 38 3.3.1 空間距離分群策略 38 3.3.2 STGCN-SD 區塊 39 3.3.3 台灣手語辨識模型的架構 40 3.3.4 台灣手語辨識模型的訓練 41 3.4 台灣手語辨識的實驗結果 42 3.4.1 與使用ST-GCN的手語辨識系統比較 42 3.4.2 人體骨架的標準化 42 3.4.3 手語關鍵點樣型的比較 43 3.4.4 與先進的手語辨識系統比較 44 第四章 機械手臂自主取物的相關原理 61 4.1 機械手臂簡介 61 4.2 機器視覺 61 4.2.1 座標轉換 61 4.2.2 深度影像濾波器 63 4.3 物件辨識 64 4.4 機器手臂的控制系統：ROS 65 4.4.1 ROS 架構概述 65 4.4.2 ROS 的運算單元 – Node 65 4.4.3 ROS 的通訊方式 – Topic 66 4.4.4 節點之間交換的訊息 66 4.4.5 ROS 的通訊模式 – Services 67 4.4.6 ROS的Action機制 67 第五章 建構手語驅動機械手臂自主取物的系統 75 5.1 系統架構 75 5.2 實驗設備 75 5.2.1 機械手臂TM5-900 75 5.2.2 電動夾爪CHG2-S30-002-L-A001 75 5.2.3 深度相機RealSense D435 76 5.3 機械手臂TM5-900的機器視覺 76 5.3.1 Eye to hand校正 77 5.3.2 物件辨識與定位 78 5.3.3 台灣手語所對應的取物任務 79 5.4 手語驅動機械手臂自主取物系統的開發 79 5.4.1 台灣手語即時辨識系統 80 5.4.2 手語對機械手臂自主取物的控制 81 5.5 實驗成果與討論 82 5.5.1 實驗1：命令機械手臂將指定物品放到人的手上 83 5.5.2 實驗2：命令機械手臂放棄之前任務 83 5.5.3 實驗3：命令機械手臂更改取物任務 84 第六章 結論與未來展望 103 6.1 結論 103 6.2 未來展望 103 參考文獻 105 附錄一：台灣手語辨識系統的訓練過程與測試結果 113 A.1 辨識系統以27點手語骨架的訓練過程與測試結果 113 A.2 辨識系統以33點手語骨架的訓練過程與測試結果 117 A.3 辨識系統以35點手語骨架的訓練過程與測試結果 122 A.4 辨識系統以37點手語骨架的訓練過程與測試結果 126 A.5 辨識系統以43點手語骨架的訓練過程與測試結果 131 A.6 辨識系統以45點手語骨架的訓練過程與測試結果 136 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 時空圖形卷積神經網路 | zh_TW |
| dc.subject | 手語辨識 | zh_TW |
| dc.subject | 機器人操作系統 | zh_TW |
| dc.subject | 機械手臂 | zh_TW |
| dc.subject | 聾啞溝通輔助 | zh_TW |
| dc.subject | 人體姿態估計 | zh_TW |
| dc.subject | ST-GCN | en |
| dc.subject | Human Pose Estimation | en |
| dc.subject | Assistive Technology | en |
| dc.subject | Robotic Arm | en |
| dc.subject | ROS2 | en |
| dc.subject | Sign Language Recognition | en |
| dc.subject | Deep Learning | en |
| dc.title | 基於時空圖卷積網路與深度影像之台灣手語辨識與機械手臂手勢控制介面 | zh_TW |
| dc.title | Taiwan Sign Language Recognition and Human-Robot Collaborative Gesture Control for Robotic Arms Based on Spatial-Temporal Graph Convolutional Networks and Stereo Vision | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 113-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 郭重顯;吳亦莊;廖展誼 | zh_TW |
| dc.contributor.oralexamcommittee | Chung-Hsien Kuo;Yi-Zhuang Wu;Chan-Yi Liao | en |
| dc.subject.keyword | 深度學習,時空圖形卷積神經網路,手語辨識,機器人操作系統,機械手臂,聾啞溝通輔助,人體姿態估計, | zh_TW |
| dc.subject.keyword | Deep Learning,ST-GCN,Sign Language Recognition,ROS2,Robotic Arm,Assistive Technology,Human Pose Estimation, | en |
| dc.relation.page | 140 | - |
| dc.identifier.doi | 10.6342/NTU202503890 | - |
| dc.rights.note | 同意授權(全球公開) | - |
| dc.date.accepted | 2025-08-11 | - |
| dc.contributor.author-college | 工學院 | - |
| dc.contributor.author-dept | 機械工程學系 | - |
| dc.date.embargo-lift | 2030-08-05 | - |
| 顯示於系所單位： | 機械工程學系 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-113-2.pdf 此日期後於網路公開 2030-08-05 | 7.03 MB | Adobe PDF |
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