Title: Emotion Detection on User Front-Facing App Interfaces for Enhanced Schedule Optimization: A Machine Learning Approach Show Chinese title

Title: 用户正面应用界面的情感检测用于增强日程优化：一种机器学习方法

Abstract: Human-Computer Interaction (HCI) has evolved significantly to incorporate emotion recognition capabilities, creating unprecedented opportunities for adaptive and personalized user experiences. This paper explores the integration of emotion detection into calendar applications, enabling user interfaces to dynamically respond to users' emotional states and stress levels, thereby enhancing both productivity and engagement. We present and evaluate two complementary approaches to emotion detection: a biometric-based method utilizing heart rate (HR) data extracted from electrocardiogram (ECG) signals processed through Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks to predict the emotional dimensions of Valence, Arousal, and Dominance; and a behavioral method analyzing computer activity through multiple machine learning models to classify emotions based on fine-grained user interactions such as mouse movements, clicks, and keystroke patterns. Our comparative analysis, from real-world datasets, reveals that while both approaches demonstrate effectiveness, the computer activity-based method delivers superior consistency and accuracy, particularly for mouse-related interactions, which achieved approximately 90\% accuracy. Furthermore, GRU networks outperformed LSTM models in the biometric approach, with Valence prediction reaching 84.38\% accuracy. Show Chinese abstract

Abstract: 人机交互（HCI）已显著发展，以整合情绪识别能力，为自适应和个性化的用户体验创造了前所未有的机会。 本文探讨了将情绪检测整合到日历应用程序中，使用户界面能够动态响应用户的情绪状态和压力水平，从而提高生产力和参与度。 我们提出了并评估了两种互补的情绪检测方法：一种是基于生物特征的方法，利用从心电图（ECG）信号中提取的心率（HR）数据，通过长短期记忆（LSTM）和门控循环单元（GRU）神经网络预测情绪维度，包括效价、唤醒和支配；另一种是基于行为的方法，通过多种机器学习模型分析计算机活动，根据细粒度的用户交互（如鼠标移动、点击和键盘输入模式）对情绪进行分类。 我们的比较分析显示，尽管两种方法都表现出有效性，但基于计算机活动的方法在一致性和准确性方面表现更优，特别是在与鼠标相关的交互中，准确率达到了约90%。 此外，在基于生物特征的方法中，GRU网络的表现优于LSTM模型，效价预测的准确率达到84.38%。