臺灣博碩士論文加值系統

本文主要整合數位機上盒與情緒感知介面以做為收視調查的平台，讓數位媒體業者能更直接與快速的了解到觀眾對於媒體內容的反應，以提升收視調查的時效。數位機上盒提供了許多的媒體內容，而媒體內容的表現為媒體業者關心的焦點，因此本文攝取與分析觀眾在觀看影片時的生理信號並進行情緒辨識分類，用以解析觀眾對於媒體內容的情緒反應。
文中先以挑選好的影片來誘發四位觀眾的高興、愉悅、厭惡、恐懼與一般等五種情緒，並攝取觀眾在觀看影片時的心電訊號。而心電訊號經過分析處理、取得特徵與正規化之後，將七種特徵參數透過KNN(K-Nearest Neighbor algorithm)的分類器來進行情緒的分類，以辨識出觀眾觀看影片的情緒狀態。
結果中顯示，利用影片刺激四位觀眾後，利用KNN 演算法將七種特徵值進行情緒的分類，當四位觀眾一起辨識五種情緒狀態時，其辨識率為76.09%。另外，文中也針對單一觀眾進行分析，當以自身情緒資料來分辨情緒，其辨識率最高為45.06%；而以非自身之情緒資料來分辨情緒，其辨識率最高僅為27.59%。由此顯示個別差異性會於情緒辨識有極大影響，需要持續增加觀眾的個數，以降低個別化的差異性。

Nowadays, with digital Set-Top Box, variety of media contents could be provided with ease and the performance of these contents becomes the focus of concern to media industry. Under the circumstances, the aim of this study is to integrate digital Set-Top Box with emotion perception interface as an audience measurement platform to enhance the measurement efficiency and quickly learn the reaction when audience viewing these contents. This study adopts and analyses the biophysical signals when audience watching video to make emotion recognition classification, which could use for recognizing audiences’ emotion reaction to media content.
The use of pre-selected video would induce four audiences'' affective response included laughing, pleasure, disgust, fear, and normal. Meanwhile, Electrocardiogram of audiences through whole process will be recorded as well. After analyzation, acquisition features, and normalization, the seven biophysical signals from Electrocardiogram would be classified by KNN(K-Nearest Neighbor algorithm) classifier to recognize audience’s affective response.
As a result, when recognized by four audience together, the accuracy of using video to induce audiences'' affective response and using KNN to classify seven biophysical signals are 76.09%. Moreover, when recognized by single audience, the accuracy of using self-testing data and using training data are 45.06% and 27.59%.
Hence, individual difference will cause huge effect to the emotion recognition. To reduce deviation and increase accuracy, more experiment data would be extremely necessary.

中文摘要 i
英文摘要 ii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 ix
第一章緒論 1
1.1 研究背景 1
1.2 研究動機與目的 3
1.3 相關文獻 3
1.4 論文架構 9
第二章相關背景知識 11
2.1 自律神經系統 11
2.1.1 交感神經系統 12
2.1.2 副交感神經系統 13
2.2 心電圖相關介紹 13
2.2.1 心臟之傳導方式 13
2.2.2 心電圖之量測方式 15
2.3 心率變異度分析 18
2.3.1 心率變異度之定義 18
2.3.2 心率變異度之量測 18
2.3.3 心率變異度之分析方式 19
2.4 情緒辨識相關介紹 23
2.4.1 情緒的刺激 23
2.4.2 情緒的量測 25
2.4.3 情緒的分類 28
第三章系統設計與分析 30
3.1 系統架構概述 30
3.1.1 情緒資料庫 31
3.2 心電訊號之攝取與處理 32
3.2.1 情緒感知平台 32
3.2.2 心跳感測彈性胸帶 33
3.2.3 藍芽傳輸器 35
3.2.4 情緒刺激 36
3.2.5 情緒感知系統設計 38
3.3 情緒辨識的處理 39
3.3.1 攝取情緒片段 40
3.3.2 獲取特徵值 41
3.3.3 特徵值正規化 41
3.3.4 分類演算法 42
3.3.5 情緒辨識之流程 43
3.3.5.1 訓練流程 43
3.3.5.2 測試流程 43
3.3.6 交叉驗證 44
3.3.7 特徵分析 45
第四章實驗結果與討論 47
4.1 情緒辨識結果 47
4.2 討論 53
4.2.1 實驗結果討論 53
4.2.2 不同觀眾的差異程度 56
4.2.3 單一觀眾的情緒辨識 58
4.2.4 分類演算法的參數最佳化 60
第五章結論與未來展望 61
5.1 結論 61
5.2 未來展望 62
參考文獻 64

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