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October 2, 2025Open Access

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

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Emotion detection in calendar applications enhances user engagement and productivity by responding to emotional states.
The computer activity-based method achieved approximately 90% accuracy for mouse-related interactions, showing superior effectiveness.
GRU neural networks outperformed LSTM models with Valence prediction accuracy of 84.38%, indicating strong predictive capabilities.
Two approaches were evaluated: a biometric method using HR data from ECG and a behavioral method based on user interaction.

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Observational analysis highlights emotion detection improving user engagement in calendar apps, suggesting better productivity outcomes.

Yang et al. (2025) studied this question.

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