An Approach for Real Time Computer User Action Monitoring

Computers play a significant part in the modern world. The rapid increase in computer use, especially after the COVID-19 pandemic, raised concerns about the potential negative impact on user health. Sitting posture, eye health, stress, and sleep are the most important factors when considering the health of daily computer users. It is more important to prevent issues from occurring than to find solutions once they have occurred. To address this issue, a Real-Time Computer User Action Monitoring Approach is proposed that uses machine learning techniques to monitor and preserve user health effectively. Importantly, this solution leverages only the built-in webcam, eliminating the need for costly external equipment and ensuring affordability. The proposed solution consists of four key components: sitting posture detection and correction, eye tracking for eye health and comfort, stress detection and reduction techniques, and drowsiness detection for enhanced productivity and safety. This approach assists users in avoiding harmful behaviors, fosters healthful computer use by providing real-time feedback, and offers personalized recommendations.