在人機介面互動方面，透過使用者的情緒與認知表現來瞭解其感受以便回饋是相當重要的研究課題，本論文研究目的主要在於使用多重生理訊號來發展情緒辨識系統；本研究首先建構受測者獨立操作之測量與分析系統，然後建立受測者情緒認知相關多重生理訊號資料庫，其中輸入訊號包含應用非侵入式穿戴裝置，可以經由身體表面擷取與自律神經相關之影響情緒的反射訊號。情緒辨識實驗應用國際情感圖庫系統(IAPS, International Affective Picture System)誘發三十位受試者好笑、愉悅、噁心、害怕等四類情緒表現，同時利用生理訊號感測器量測與記錄末梢血流量、肌電圖、心電圖、膚電反應及體表溫度等生理訊號。所記錄之多重生理訊號經過正規化、生理參數擷取及特徵值選取過程後，將19個生理參數輸入支援向量機(SVM , Support vector machines)分類器進行分類，以達到辨識情緒的目的。從研究結果顯示，利用國際情感圖庫系統作為影片刺激，以成對T檢定為特徵值選取，其辨識率分別為76.2%、66.7%、71.4%、69%；另一方面以變異數分析為特徵值選取，分類辨識率則為82.9%、71.4%、81.4%、78.6%。本研究最後對於研究情緒辨識時所碰到的難題加以討論，並對未來進行受測者情緒辨識系統的研究提供方向與策略。

In multimodal human-computer interaction, to understand emotion and cognition expression of users for feedback control is an important issue. The purpose of this study is to develop emotion recognition system using multiple physiological signals. In this study, a stand-alone measurement and analysis system for subject operation is firstly constructed, and then an emotion related multiple physiological signals database is built. In which, the input signals that may reflect the autonomic nervous system associated with influence of emotion are acquired using non-invasive and wearable devices from the body surface. The IAPS (International Affective Picture System) is employed to elicit the affective responses of happiness, pleasure, disgust, and fear from thirty healthy subjects. The multiple physiological signals including photoplethysmography, electromyography, electrocardiogram, galvanic skin response, and skin temperature signal are measured and recorded simultaneously. After signal normalization, signal preprocessing, feature extraction, and feature selection, nineteen parameters are input to the support vector machine classifier for the corresponding emotional response classification. From the experimental results of using IAPS to elicit the emotion, it is shown that the accuracies of emotion recognition rate are 76.2%, 66.7%, 71.4%, and 69% based on the t-test, and are 82.9%, 71.4%, 81.4%, and 78.6% based on the ANOVA. Finally, the difficulties associated with the investigation of emotion recognition system are discussed, and the future direction and research suggestions are also provided.

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