An Intelligent Platform for Behavior Modification and Office Syndrome Risk Reduction Using MediaPipe and Computer Vision

Office Syndrome, a musculoskeletal disorder prevalent among office workers, poses significant risks to health, productivity, and quality of life. Traditional preventive approaches, such as ergonomic guidelines and reminder-based systems, often fail due to limited user adherence and practicality. To address this gap, this study developed an intelligent platform that integrates MediaPipe and computer vision to monitor sitting posture, eye-to-screen distance, and sitting duration in real time. The system provides automated notifications and stretching recommendations, combining detection, feedback, and behavioral intervention into a sensor-free and cost-effective solution. The platform was evaluated in terms of technical performance and user behavioral impact. Results demonstrated high system accuracy, with the eye-distance detection module achieving 95.2% accuracy, followed by long sitting alerts (92.5%) and proximity alerts (90.1%). User evaluations confirmed that real-time notifications increased awareness and encouraged healthier working behaviors. These findings highlight the potential of computer vision based approaches for ergonomic health promotion. The proposed platform contributes not only to preventive strategies for Office Syndrome but also to advancing user-centered, technology-driven health solutions adaptable to both office and remote work environments. This study not only demonstrates technical performance but also introduces a novel integration of Media Pipe based posture and facial detection with behavioral modification features, which previous ergonomic systems have not addressed. The proposed framework contributes a new perspective for integrating real time feedback with computer vision in promoting sustainable ergonomic behavior.

@article{Chaikhamwang2025,
  title     = {An Intelligent Platform for Behavior Modification and Office Syndrome Risk Reduction Using MediaPipe and Computer Vision},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {10},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Sumran Chaikhamwang and Wijitra Montri and Chalida Janthajirakowit and Srinuan fongmanee},
  doi       = {10.14569/IJACSA.2025.0161038},
  url       = {https://doi.org/10.14569/IJACSA.2025.0161038}
}