Recent research has demonstrated the capability of physiological signals to infer both user emotional and attention responses. This presents an opportunity for leveraging widely available physiological sensors in smartwatches, to detect real-time emotional cues in users, such as stress and excitement. In this paper, we introduce SensEmo, a smartwatch-based system designed for affective learning. SensEmo utilizes multiple physiological sensor data, including heart rate and galvanic skin response, to recognize a student's motivation and concentration levels during class. This recognition is facilitated by a personalized emotion recognition model that predicts emotional states based on degrees of valence and arousal. With real-time emotion and attention feedback from students, we design a Markov decision process-based algorithm to enhance student learning effectiveness and experience by by offering suggestions to the teacher regarding teaching content and pacing. We evaluate SensEmo with 22 participants in real-world classroom environments. Evaluation results show that SensEmo recognizes student emotion with an average of 88.9% accuracy. More importantly, SensEmo assists students to achieve better online learning outcomes, e.g., an average of 40.0% higher grades in quizzes, over the traditional learning without student emotional feedback.

翻译：近期研究表明，生理信号能够推断用户的情绪与注意力反应。这为利用智能手表中广泛配置的生理传感器（如心率与皮电反应）检测用户实时情绪线索（如压力与兴奋）提供了可能。本文提出SensEmo——一种基于智能手表的情感学习系统。该系统通过整合多源生理传感器数据（包括心率与皮电反应），结合基于效价-唤醒度维度的个性化情绪识别模型，实时识别学生在课堂中的动机水平与专注程度。基于学生实时情绪与注意力反馈，我们设计了一种基于马尔可夫决策过程的算法，通过向教师提供教学内容与节奏的调整建议，以提升学生的学习效果与体验。我们在真实课堂环境中对22名参与者进行了系统评估。实验结果表明，SensEmo对学生情绪识别的平均准确率达到88.9%。更重要的是，相较于传统无情绪反馈的学习模式，SensEmo能帮助学生取得更优的在线学习效果，例如在随堂测验中平均成绩提升40.0%。