線上學習是藉由網際網路所傳遞的教學模式，具有超越時間、空間、互動性
高、並可獲得立即性回饋的特性，已成為新一代教與學的新選擇，而學習者的情
緒變化對於學習頗富影響力，數位學習領域也嘗試利用人工智慧的方法在學習過
程中偵測學習者情緒。因此本研究以嵌入式系統結合感測器、訊號處理電路與
8051晶片，完成一套脈搏訊號感測暨傳輸系統，將情緒脈搏訊號透過無線網路傳
送至伺服器端，並進行以人類脈搏分析為主的情緒偵測。在本研究的實驗中，共
有10位受測者參與，以線上影片與電腦遊戲引發出緊張、平靜、歡樂三種情緒，
並以攝影機錄製整個實驗過程，根據影片內容來擷取出情緒脈搏訊號，脈搏訊號
首先利用傅立葉轉換將脈搏訊號由時域轉換至頻域，並依據不同頻率的諧波成分
進行特徵擷取，再透過支向機的技術做情緒分類預測，初步結果在606筆取得之
三種情緒脈搏訊號之交叉驗證正確率為76.824%；為了進一步提高情緒偵測的正
確率，本研究進一步濾除取樣不夠精確的雜訊後得到419筆資料，並再次進行三
種情緒之交叉驗證正確率提昇為79.7136%。最後，本研究進一步結合所發展的人
類脈搏情緒偵測系統於線上討論區，輔助老師與學生進行線上討論區教學時可以
即時瞭解學習者的情緒反應，進而幫助老師依據學習者不同情緒反應輔導學生進
行有效學習。目前本研究所提出依據人類脈搏生理訊偵測學習情緒的方法已經得
到不錯的辨識結果，這證明使用生理訊號探討學習者情緒反應為一可行的研究方
向，後續在諸如語言學習焦慮情緒偵測及基於情緒反應之個人化學習等應用上具
有極高的研究價值。

In recent years, E-learning has been a popular learning mode due to the fast growth of the Internet and it has advantages in terms of high interaction, getting feedback immediately, and breaking the limitations of learning time and space. In addition, many studies indicated that the variations of learning emotions have key affection to the learning outcomes of E-learning and many studies also proposed that detecting human emotions by physiological signals is a practicable scheme.Accordingly, the study employed sensor, signal processing, communication and system on chip (SOC) techniques to develop a embedded human emotion detection system based on human pulse signals, which can detect three human emotions including nervous, peaceful, and joyous for supporting teachers to conduct interactive learning with students on online discussion board. There are totally ten volunteers who were invited to participate in this experiment. In the experiments, several selected web movies and computer games were applied to cause emotion responses.
Meanwhile, the pulse signals caused by emotion variations are retrieved by the developed embedded human emotion detection system and stored in the database for emotion analyses. To process the pulse signals for emotion detection, the extracted human pulse signals are first transformed by Fourier transform from time domain to frequency domain, then the transformed data is used to extract emotion features for training an emotion detection model by support vector machine (SVM). The accuracy rate of the modeling emotion detection mechanism evaluated by cross validation is 76.8254%. To further filter out noisy human pulse data, the accuracy rate of emotion detection evaluated by cross validation can be promoted from 76.824% to 79.7136%. Currently, the proposed human emotion detection mechanism has been successfully applied to the online discussion board to support teachers for conducting interactive
III learning with students.

第一章 緒論
第二章 文獻探討
第三章 系統架構與實驗過程
第四章 實驗結果分析
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