Abstract

The growing trend of remote work has brought digital burnout on board as a significant occupational health issue, marked by psychological exhaustion and emotional strain from extended use of digital devices. This study conceived and created an AI-based early warning system that examines behavioral and interaction data to detect threats of digital burnout among remote workers and suggest early interventions. The solution employed the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, integrating a behavioral tracking module with system tracking APIs, an emotion inference engine through BERT-based sentiment analysis, a Random Forest machine learning model for burnout scoring, and a personalized recommendation system providing interventions. The solution was developed using Python libraries Scikit-learn, Transformers, and Streamlit, and testing was carried out using a synthetic dataset of 1,000 remote workers. The outcomes exhibited good performance with 79.5% accuracy, 0.76 precision on at-risk cases, 0.62 recall, and an ROC-AUC value of 0.88, reflecting good discrimination between at-risk and not-at-risk populations. The Streamlit web application deployed successfully combined all modules, offering a user-friendly interface for behavioral input data and sentiment analysis while presenting actionable wellness reports with tailored suggestions. This research contributes a proactive solution for organizational wellbeing management in remote work environments, bridges the gap between behavioral monitoring and sentiment analysis for comprehensive burnout assessment, and establishes a foundation for ethical AI application in workplace wellness monitoring that balances employee privacy with effective health intervention.

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