FocusWave A Machine Learning Approach to Personalized Focus Optimization

Maintaining consistent concentration and mental stability has become increasingly complex in multitasking digital environments. FocusWave introduces an adaptive focus optimization framework designed to enhance user attention patterns and emotional awareness using intelligent computational models. The system integrates dual predictive models, namely XGBoost and Random Forest, for focus cycle prediction, and a BERT-based sentiment analysis model for emotional state detection from text. The XGBoost and Random Forest models analyze behavioral and contextual features such as engagement level, distraction frequency, mood score, and task category to predict the optimal duration for the next focus cycle. Simultaneously, the BERT model processes text input to classify emotions such as happiness, sadness, or anger, providing a detailed understanding of user sentiment and psychological readiness.

The Django-based web platform enables users to record activities, monitor focus performance, and receive dynamic recommendations for task continuation or recovery breaks. Through predictive insights and emotion-aware feedback, FocusWave supports balanced attention cycles, sustained cognitive performance, and improved task engagement. The framework bridges behavioral analytics and emotional computing, contributing to adaptive human–machine interaction and productivity enhancement.

Keywords: Focus Optimization, Machine Learning, Cognitive Modeling, Emotion Detection, Random Forest, XGBoost, BERT, Django Framework, TF-IDF, Human–Computer Interaction, Productivity Enhancement.