A Study on the Architecture and Operation of AI-Based Digital Well-Being Monitoring Systems

Abstract: Digital technologies such as smartphones, social media platforms, wearable devices, and online applications have become an essential part of modern life. Although these technologies improve communication, productivity, education, and entertainment, excessive and uncontrolled digital usage has also created several challenges related to mental health, stress, anxiety, sleep disorders, digital addiction, and reduced emotional well-being. Traditional digital well-being systems mainly provide basic monitoring features such as screen time tracking and app usage statistics, which often lack intelligent behavioral analysis, real-time monitoring, and personalized recommendations. This paper presents a study on AI-Based Digital Well-Being Monitoring Systems that utilize Artificial Intelligence, Machine Learning, Deep Learning, wearable sensing technologies, and behavioral analytics to monitor user digital activities and improve overall well-being. The proposed system analyzes behavioral patterns, emotional conditions, physiological signals, smartphone usage habits, and social media interactions to detect unhealthy digital behavior and provide adaptive wellness recommendations.

Keywords: Artificial Intelligence (AI), Digital Well-Being, Machine Learning, Deep Learning, Behavioral Analytics, Mental Health Monitoring, Wearable Devices, Emotion Recognition, Stress Detection, Personalized Recommendation Systems.