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研究生:陳河谷
研究生(外文):Ho-Ku Chen
論文名稱:基於光體積變化描述波形與皮膚電流活動生理訊號之情緒識別研究
論文名稱(外文):Emotion Recognition Based on Physiological Signals of Photoplethysmographic Signals and Galvanic Skin Response
指導教授:李建誠李建誠引用關係
指導教授(外文):Chien-Cheng Lee
學位類別:碩士
校院名稱:元智大學
系所名稱:通訊工程學系
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:2009
畢業學年度:97
語文別:中文
論文頁數:65
中文關鍵詞:光體積變化描述波形皮膚電流活動情緒識別脈波訊號分析多尺度熵
外文關鍵詞:PhotoplethysmographicGalvanic Skin Responsemean shiftemotion recognitionpulse analysismultiscale entropy
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  • 收藏至我的研究室書目清單書目收藏:1
本研究透過少量的生理訊號種類,並開發更有效率的生理訊號分析方法,以實現生理訊號情緒識別。論文中使用光體積變化描述波形 (photoplethysmographic, PPG) 與皮膚電流活動 (galvanic skin responses, GSR) 兩種生理訊號進行情緒識別,並提出非對稱 multibandwidth mean-shift 極點搜尋法與迴歸差異曲點偵測法兩種方式,用以偵測 PPG 訊號中的收縮波峰、舒張波谷、降中峽 (dicrotic notch) 及重搏波峰 (dicrotic peak) 等特徵點;非對稱 multibandwidth mean-shift 極點搜尋法用於連續時間序列訊號之最大值與最小值的偵測,不僅突破 mean-shift 只能搜尋密度函數的模數 (mode) 限制外,並可將搜尋範圍延伸至後續時間之訊號;迴歸差異曲點偵測法利用線性迴歸 (linear regression) 概念偵測降中峽與重搏波峰,取代設計大量門閥值與小波轉換等繁瑣之處理。利用上述兩種分析方式以及多尺度熵 (multiscale entropy) 分析,從 PPG 與 GSR 兩種生理訊號擷取出分類特徵。
實驗中,分別對十位受測者進行七種情緒誘發 (neutral, love, joy, surprise, sadness, anger, fear),並從生理訊號中擷取出情緒特徵,再採用支援向量機 (support vector machine) 進行分類,利用受測者本身情緒特徵進行分類器的訓練與測試,平均辨識率可達到98%。
This paper presents a system for emotion recognition using two physiological signals, including photoplethysmographic (PPG) signals and galvanic skin response (GSR). We propose two novel methods for detecting the significant points in photoplethysmographic signals (diastolic trough, systolic peak, dicrotic notch, and dicrotic peak.) Firstly, the method named asymmetric multibandwidth mane-shift extremes seeking provides the ability for detecting maximum and minimum modes in time series signals. Secondly, the method named regression difference bendpoint detection provides a fast and simplified way for locating the dicrotic notch and dicrotic peak. In addition, multiscale entropy analysis is adopted to extract the features from GSR signals. Using fewer physiological signals and significant features with emotional responses are the main ideas in our recognition system. Ten subjects join this experiment and 29 features obtained from the two bio-signals with one person. Support vector machine was used for the classifications. The recognition rate achieved 98%.
書頁名 ii
審定書 iii
中文摘要 iv
英文摘要 v
誌 謝 vi
目 錄 vii
表 目 錄 x
圖 目 錄 xi
第一章、緒論 1
1.1 研究背景 1
1.2 文獻回顧 2
1.3 研究目的 6
1.4 論文架構 7
第二章、情緒辨識與生理訊號介紹 8
2.1 情緒識別介紹 8
2.1.1 類別式與維度式之情緒分類 8
2.1.2 使用者獨立與相依之情緒識別 9
2.1.3 生理訊號情緒識別之文獻探討 13
2.2 生理訊號介紹 19
2.2.1 情緒因素與生理反應 19
2.2.2 生理訊號及其特徵 23
第三章、生理訊號分析 31
3.1 脈波訊號分析之探討 31
3.1.1 收縮波峰與舒張波谷之偵測 31
3.1.2 降中峽偵測 32
3.2 非對稱multibandwidth mean-shift 極點搜尋法與脈波波形分析 34
3.2.1 mean-shift演算法之演進 34
3.2.2 非對稱multibandwidth mean-shift 極點搜尋法 37
3.3 迴歸差異曲點偵測法 39
3.4 脈波波形之特徵擷取 41
3.5 心率變異度之特徵擷取 43
3.6 Multiscale entropy之 GSR 訊號特徵擷取 43
3.6.1 樣本熵分析 44
3.6.2 多尺度熵分析 46
第四章、實驗 48
4.1 實驗架構與步驟 48
4.1.1 實驗設備 48
4.1.2 情緒誘發流程 49
4.2 情緒分類結果 50
4.2.1 實驗數據蒐集 50
4.2.2 挑選訓練與測試樣本 53
4.2.3 分類結果與討論 54
第五章、結論與未來展望 59
參考文獻 60
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