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研究生:鄭偉凱
研究生(外文):Wei-KaiCheng
論文名稱:自動化情緒分類研究-以高功能自閉症學生之數位學習應用為例
論文名稱(外文):On Automatic Emotion Classification: An Application on E-learning for Students with High-Functioning Autism
指導教授:陳裕民陳裕民引用關係
指導教授(外文):Yuh-Min Chen
學位類別:碩士
校院名稱:國立成功大學
系所名稱:製造資訊與系統研究所碩博士班
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:中文
論文頁數:83
中文關鍵詞:情緒分類生理訊號與臉部表情高功能自閉症學生數學數位學習
外文關鍵詞:Emotion classificationPhysiological and facial Expression measuresStudents with high-functioning autismMathematics e-learning
相關次數:
  • 被引用被引用:1
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  • 收藏至我的研究室書目清單書目收藏:4
自閉症學生數學數位學習之成效提升,情緒扮演重要角色,為避免其於數學數位學習過程,因情緒問題而導致學習失效,已逐漸成為特殊教育與資訊通訊技術跨領域結合之重要議題。本文針對數學數位學習環境,因缺乏感知自閉症學生情緒,以致無法情感適性化學習,提出一自閉症學生數學數位學習之適性化情緒調適模式,並發展高功能自閉症學生情緒分類機制,透過真實數學數位學習內容,分別誘發高功能自閉症學生平靜(Baseline)、高興(Happy)、焦慮(Anxious)及生氣(Angry)等四種情緒,且同時記錄其生理訊號與臉部表情變化,同步萃取58個情緒特徵;再經由資訊增益與單因子變異數兩種特徵選取方法,保留29個與情緒相關之特徵;接著分別使用支援向量機(SVM)、最鄰近分類(KNN)及分類迴歸樹(CART)三種分類模型,各別嵌入於拔靴集成(Bootstrap)與調適性多模增進(Adaboosting)兩種集成分類模型,以進行四種情緒之分類,經實驗與比較後,由單因子變異數所選用之情緒特徵集合,配合使用SVM嵌入拔靴集成情緒分類模型,其整體情緒分類辨識率可達81%。該情緒分類機制於未來可支援情緒調適模式之運行,協助自閉症學生於學習過程進行情緒調適,提高數學數位學習成效。
Emotional problems of students with autism play an important role in their learning in mathematics e-learning environments. This study proposed an emotional adjustment model for students with high-functioning autism in mathematics e-learning with an emotion classification mechanism. The present paper reports the development of the emotion classification mechanism through evoking autistic students’ emotions in a mathematical e-learning environment and recording changes in their physiological signals and facial expressions. A total of fifty-eight measures were obtained from an experiment, and twenty-nine measures were further extracted from one-way ANOVA and information gain (IG) methodology. Support vector machines (SVM), k-nearest neighbors (KNN), and classification and regression trees (CART) were combined with bootstrap aggregating (Bagging) individually and used to classify four emotional categories: calm, happy, anxious, and angry from the twenty-nine features. The accuracy rate of the SVM ensembles using Bagging to classify emotions reached the highest (81%). The emotion classification mechanism developed in the present report could support the emotional adjustment model which aims at classifying and adjusting autistic students’ emotions during mathematics learning and enhancing their learning effectiveness.
摘要 I
Abstract II
致謝 III
目錄 IV
表目錄 VII
圖目錄 VIII
第一章 緒論 1
1.1 研究背景 1
1.2 研究動機 1
1.3 研究目的 2
1.4 問題分析 3
1.5 研究項目與方法 4
1.6 研究步驟 5
1.7 論文架構 6
第二章 文獻探討 7
2.1 高功能自閉症之定義與情緒特質 7
2.1.1 高功能自閉症之定義 7
2.1.2 高功能自閉症之社會情緒特質 8
2.2 學業情緒之內涵 9
2.2.1 情緒定義與類別 9
2.2.2 高功能自閉症之負向數學學業情緒特質 11
2.3數學數位學習於自閉症學生之應用 12
2.3.1 數位學習之特性 12
2.3.2 高功能自閉症之數學數位學習 13
2.3.3 情意運算與數位學習 13
2.4 生理訊號 14
2.4.1 心電圖 14
2.4.2 肌電圖 15
2.4.3 末梢血流量 16
2.4.4 皮膚導電反應 16
2.5 臉部表情動作編碼 17
2.5.1 臉部動畫參數 17
2.5.2 臉部動作編碼系統 17
2.6 特徵選取技術 17
2.6.1 資訊增益 18
2.6.2 單因子變異數 18
2.7自動化分類技術 19
2.7.1 支援向量機 19
2.7.2 最鄰近分類法 20
2.7.3 分類迴歸樹 21
2.7.4 拔靴集成分類法 22
2.7.5 調適性多模增進法 22
第三章 模式與機制架構設計 23
3.1 自閉症學生數學數位學習之適性化情緒調適模式設計 23
3.2 高功能自閉症學生情緒分類機制架構設計 26
第四章 情緒分類方法設計與機制開發 29
4.1 生理訊號特徵萃取方法設計 29
4.1.1 訊號濾波 29
4.1.2 心電圖特徵萃取 31
4.1.3 肌電圖特徵萃取 35
4.1.4 末梢血流量特徵萃取 38
4.1.5 皮膚導電特徵萃取 41
4.1.6 生理特徵正規化 44
4.2 臉部表情特徵萃取方法設計 45
4.2.1 臉部特徵追蹤 45
4.2.2 表情特徵萃取 46
4.2.3 表情特徵正規化 46
4.3 情緒分類方法設計 47
4.3.1 特徵轉換 48
4.3.2 特徵選取 48
4.3.2.1 資訊增益分析 48
4.3.2.2 單因子變異數分析 49
4.3.3情緒分類模型訓練 50
4.3.3.1 支援向量機分類模型 50
4.3.3.2 最鄰近分類模型 51
4.3.3.3 分類迴歸樹模型 51
4.3.3.4 拔靴集成分類模型 53
4.3.3.5 調適性多模增進分類模型 54
4.3.4正確性評估 55
4.4 機制開發 56
4.4.1 實作環境 56
4.4.2 機制實作結果 56
第五章 實驗與機制驗證 57
5.1 高功能自閉症學生情緒誘發實驗 57
5.1.1 受試對象標準 58
5.1.2 情緒誘發材料 59
5.1.3 實驗步驟 61
5.1.4 情緒標記評量 63
5.2 情緒分類機制實驗數據 63
5.2.1 取樣區間與基線期之選定 64
5.2.2 情緒樣本標記選取 65
5.2.3 特徵選取結果 66
5.3 單一模型之情緒分類結果 67
5.3.1 支援向量分類模型之情緒類型分類結果 68
5.3.2 最鄰近分類模型之情緒類型分類結果 69
5.3.3 迴歸樹分類模型之情緒類型分類結果 70
5.4 集成模型之情緒分類結果 70
5.4.1 拔靴集成分類模型之情緒類型分類結果 71
5.4.2 調適性多模增進分類模型之情緒類型分類結果 71
5.5 單一特徵為基之情緒分類結果 72
5.5.1 單一生理號特徵為基之情緒類型分類結果 72
5.5.2 單一臉部特徵為基之情緒分類結果 73
第六章 結論與未來研究方向 75
6.1 討論 75
6.2 結論 77
6.3 未來研究與方向 78
參考文獻 79

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