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

--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-T erm Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks to predict the emotional dimensions of V alence, 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 V alence prediction reaching 84.38% accuracy. Human-Computer Interaction (HCI) has traditionally focused on enhancing user experiences by creating systems that are functional, intuitive, and engaging. In recent years, the integration of emotion recognition into HCI has introduced new opportunities for developing truly personalized interactions that adapt to users' emotional states, addressing a significant gap in current interface design [1]. This problem is compounded by ineffective scheduling and time management systems.