在後 COVID-19 時代社會活動有限、分離和孤立的情況下，情感健康變得至關重要。支持人機互動的智能系統需具備滿足情緒反應需求的能力，如仿生機器人、智慧型代理人等。為了及時對情緒做出適當的應對，特別是負面情緒，電腦能夠感知情緒轉變之前兆是至關重要的。本研究的主要目標是開發情緒辨識和調適框架（Emotion R-R Framework），使電腦能夠識別基於臉部特徵的人類情緒，其中包括了情緒轉換辨識機制（ETRM）和情緒調適機制（ERM）。此外，本研究還進行了收集數據和模型驗證的實驗。ETRM 植基於滑動視窗技術和支持向量機（SVM）來建構分類器以識別情緒。本研究使用資訊增益法 (IG) 和卡方法來驗證穩健的特徵集，並分別檢驗具不同滑動視窗參數的分類器之有效性。實驗結果證實所提出的方法具有足夠的辨識力：基本情緒和轉折情緒的識別率分別為99.13%和 92.40%。此外，透過特徵選擇，訓練時間提高了 4.45 倍，基本情緒和轉折情緒的識別率分別為 97.97% 和 87.49%。為了證實本研究之應用性，本研究以數學數位學習環境作為驗證環境進行實驗，以評估本研究之有效性。情緒辨識分類器的結果平均辨識率達到了 93.34%，本次實驗的參與者表現出從基線期到干預期的負向行為在統計學上顯著減少（p=0.00002），且數學學習成績顯著提高（p=0.0045）；然而，參與者對情緒調適干預後的反應各不相同。最後本文針對研究和實務的影響作出討論。

Under the circumstance of limited, separated, and isolated social activities in the post-COVID-19 era, sentimental health becomes essential. The human interaction-enabled intelligent system requires the capability to meet the emotional response requirements, such as humanoid robots, intelligent agents. To deliver a proper response of emotion in time, primarily negative emotion, sensing emotion transition as forewarning is critical. This study's main objective is to develop the Emotion Recognition-and-Response Framework (Emotion R-R Framework) for enabling the computer to recognize human emotion transitioning from facial features, including the Emotion Transition Recognition Mechanism (ETRM) and the Emotional Response Mechanism (ERM). Also, this study conducted experiments for collecting data and model validation. The proposed method used the sliding window technique and support vector machine (SVM) to build classifiers to recognize emotions. This study used Information Gain (IG) and Chi-square to determine the robust feature set and examined the effectiveness of classifiers with different parameters of sliding windows. The experimental results confirmed that the proposed method has sufficient discriminatory capability. The recognition rates for basic emotions and transitional emotions were 99.13 and 92.40%, respectively. Also, through feature selection, training time was accelerated by 4.45 times, and the recognition rates for basic emotions and transitional emotions were 97.97 and 87.49%, respectively. This study experimented in a mathematical e-learning context to evaluate the performance of e-learning and the effectiveness of emotion regulation. The results of the emotion recognition classifier reached a 93.34% average recognition rate, and the participants of this experiment displayed a statistically significant decrease in targeted negative behaviors from baseline to intervention (p=0.00002) and significant improvements in mathematics learning performance (p=0.0045); however, responses to emotion regulation intervention varied among the participants. Implications for research and practice are discussed.

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