博碩士論文 100522029 詳細資訊




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姓名 黃秀珊(HUANG,SIOU-SHAN)  查詢紙本館藏   畢業系所 資訊工程學系
論文名稱 多模態體感動作辨識系統之人機介面研究
(Multi-modal human–Machine Interaction system for Human Motion Recognition.)
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摘要(中) 人機互動、智能生活是現行電腦科學研究領域中十分熱門的議題,而人體動作辨識更是被認為達到這些目標的重要關鍵。而在微軟推出Kinect之後,有越來越多的研究單位選擇使用較為低成本且普及的Kinect來做姿態辨識。本論文透過結合無線感測器改善Kinect 先天性的偵測弱點,針對當部分肢體平貼於地面或有肢體重疊的姿態進行姿態辨識。
本論文將對使用Kinect、無線感測器、以及Kinect結合感測器等三套系統進行比較當使用不同裝置於姿態辨識的差異,其姿態辨識率、任務完成時間、使用者感受等。
使用Kinect搭配OpenNI結合無線感測器所產生的人體骨架的三維座標(x,y,z)值,投影到Unity 3D 遊戲場景中,利用3D骨架呈現人體姿態,而其骨架資訊經過正規化後,使用自我組織特徵映射網路(SOM)進行分群,再與正確樣本進行即時性(real-time)的比對。
本論文提出的Kinect體感技術結合無線感測器模組輔助系統在姿態辨識上有平均94.6%的平均辨識率,明顯比其他兩套系統來的高,可以得出其系統整合了兩種體感技術於姿態辨識的優點。本系統在辨識複雜度高的姿勢動作時,也能有平均94.6%的成功辨識率。
摘要(英) In this study, we use wireless sensors combined with Kinect to improve the detection weakness of Kinect which is some parts of human bodies flat on the ground or gesture overlapping. In human gesture recognition, we compare Kinect system, sensor system, and Kinect combined sensor system those three systems in four ways: devices recognize difference, gesture recognition rate, task completion time, user’s satisfaction.
Three-dimensional coordinate (x, y, z) values, which is 3D human skeletons’ data using Kinect with OpenNI combining with wireless sensors, projected to Unity 3D game scene and it shows result and compares with the correct sample by using self-organizing map network (SOM) in real time. This study proposes a combination of wireless sensor and Kinect system in human gesture recognition with an average of 94.6% on the average recognition rate.
關鍵字(中) ★ 姿態辨識
★ Kinect
★ 無線感測器
★ 自我組織特徵映射網路
關鍵字(英) ★ human gesture recognition
★ Kinect
★ wireless sensors
★ SOM
論文目次 目錄
摘要 i
Abstract ii
致謝 iii
目錄 iv
圖目錄 vi
表目錄 viii
第一章 緒論 1
1-1 研究動機 1
1-2 研究目的 2
1-3 論文架構 5
第二章 相關研究 6
2-1 傳統WEBCAM於姿態辨識上的研究 6
2-2 無線感測器於人體姿態辨識上的研究 7
2-3 KINECT 於人體姿態辨識上的研究 12
2-4 演算法(類神經網路)的相關研究 15
第三章 研究方法:系統設計 17
3-1 動作感測方法 17
3-1-1 Kinect體感技術輔助系統(簡稱Kinect系統) 17
3-1-2 無線感測器模組輔助系統(簡稱Sensor系統) 18
3-1-3 Kinect體感技術結合無線感測器模組輔助系統(K+S系統) 23
3-2 動作姿態辨識方法 23
3-2-1自我組織特徵映射圖(SOM)演算法介紹 23
3-2-2 使用SOM來實現動作姿態分群 25
3-3姿態辨識系統 29
3-3-1 系統架構 29
3-3-2 多重表徵使用者介面設計 30
3-3-3 任務設計 31
第四章 研究方法:實驗設計 37
4-1 受試者 37
4-2 實驗流程 37
4-3 問卷設計 39
第五章 結果與討論 41
5-1姿態辨識演算法之辨識率驗證 41
5-2任務表現:任務完成率 42
5-3任務表現:任務平均完成時間 45
5-4 運動軌跡 46
5-5 問卷結果與分析 47
第六章 結論與未來展望 50
第七章 參考文獻 51
附錄 57
圖目錄

圖 1 研究架構圖 4
圖 2 星型骨架〔22〕 7
圖 3 9DOF RAZOR IMU (HMC5883L) 8
圖 4 各種天線型態的XBEE模組 (A)晶片型 (B)鞭型天線 (C)PCB印刷型天線 10
圖 5 L. WENFENG 〔25〕提出的七個感測器之實際裝置圖 11
圖 6 跌倒偵測之實際配戴圖〔30〕 12
圖 7 感測器服飾〔31〕 12
圖 8即時辨識系統〔32〕 14
圖 9 KINECT 運作流程圖 18
圖 10 全身感測器裝置圖 19
圖 11 XBEE EXPLORER(正反面) 19
圖 12 無線傳輸流程圖 20
圖 13 無線感測器之組成 20
圖 14 ARDUINO 軟體畫面 21
圖 15 X-CTU軟體畫面 22
圖 16 X-CTU MODEN CONFIGURATION設定畫面 22
圖 17 KINECT與無線感測器結合之傳輸流程圖 23
圖 18 KINECT 全身關節圖〔44〕 26
圖 19 鄰近區域函數 (A)正方形 (B)六邊形的型式 28
圖 20系統架構圖 30
圖 21 操作介面之介紹 31
圖 22 動作1 32
圖 23 動作2 32
圖 24 動作3 33
圖 25 動作4 33
圖 26 動作5 34
圖 27 動作6 34
圖 28 動作7 35
圖 29 動作8 35
圖 30 動作9 36
圖 31 個別系統實驗流程圖 38
圖 32 動作七之任務成功圖(K+S系統) 39
圖 33 動作七之任務失敗圖(KINECT系統) 39
圖 34 科技接受度模型〔45〕 40
圖 35動作四之下肢運動軌跡(A)KINECT (B)無線感測器 47
圖 36 動作五之下肢運動軌跡(A)KINECT (B)無線感測器 47

表目錄

表 1 SOM參數設定 28
表 2 任務動作之類神經元編號 29
表 3 三種演算法的辨識率 42
表 4 在不同環境下之任務完成率 42
表 5不同環境下之辨識率的單因子變異數分析 43
表 6 個別任務動作之單因子變異數分析 44
表 7 任務平均完成時間 45
表 8 KINECT與K+S作T-TEST檢定 46
表 9 問卷信度分析 47
表 10問卷的單因子變異數分析結果 48
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指導教授 葉士青(Shih-Ching Yeh) 審核日期 2013-8-9
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