A Multi-Factor Vision-Based Framework for Behavioral Risk Assessment of Computer Vision Syndrome Using the TensorFlow Framework

This study proposes a real-time multi-factor behavioral monitoring framework for Computer Vision Syndrome (CVS) using computer vision techniques and TensorFlow for browser-based implementation. Four vision-based detection pipelines—Dlib-based, MediaPipe-based, CNN-based, and TensorFlow-based implementations—were evaluated to identify a suitable configuration for real-time deployment. The selected browser-based implementation integrated MediaPipeFaceMesh for facial landmark extraction and MoveNet SinglePose Lightning for supplementary pose-related detection. During the pipeline-selection stage, the Dlib-based pipeline showed high task-specific accuracy in blink detection (0.9034) and head pose estimation (0.9005), while the MediaPipe-based pipeline provided the highest processing speed for these tasks, with 73.09 FPS and 75.36 FPS, respectively. The CNN-based baseline showed limited real-time suitability, with low F1-scores and FPS values ranging from 4.22 to 7.32 across tasks. These preliminary comparison results informed the selection of the browser-based pipeline, which provided the most practical trade-off among detection performance, real-time processing capability, browser-based execution, and deployment flexibility. In blink detection, the selected pipeline achieved a precision of 0.8906, a recall of 0.9490, an F1-score of 0.9189, and 13.94 FPS. The proposed framework integrates five core operational indicators: viewing distance, vertical viewing deviation, horizontal viewing deviation, blink rate, and continuous usage duration. These indicators support rule-based real-time alerts and session-based behavioral pattern analysis. After implementation, the prototype operated in real time, detected concurrent CVS-related behavioral conditions, generated interpretable rule-based alerts, and summarized recurring behavioral patterns across a monitoring session. A controlled alert-level evaluation further indicated that the warning layer operated consistently for most rule-based alert conditions, although low-blink and prolonged-focus alerts require further refinement. These findings highlight the potential of combining browser-based visual detection with interpretable operational indicators for practical CVS-related behavioral monitoring.