Open Access
| Issue |
ITM Web Conf.
Volume 82, 2026
International Conference on NextGen Engineering Technologies and Applications for Sustainable Development (ICNEXTS’25)
|
|
|---|---|---|
| Article Number | 03012 | |
| Number of page(s) | 6 | |
| Section | Information and Technology | |
| DOI | https://doi.org/10.1051/itmconf/20268203012 | |
| Published online | 04 February 2026 | |
- T. Thompson, R. Miller, and P. Davis, “Software-based screen activity trackers for desktop usage analysis,” Proc. ACM Int. Conf. Human-Computer Interaction, 2008. [Google Scholar]
- S. Balaji and A. Ramesh, “Real-time user presence detection using Haar-cascade based face recognition,” Int. J. Computer Vision and Applications, vol. 12, no. 4, pp. 221–229, 2012. [Google Scholar]
- K. Wong, J. Li, and M. Chan, “Infrared-assisted presence detection for indoor monitoring systems,” IEEE Sensors Journal, vol. 15, no. 6, pp. 3560–3568, 2015. [Google Scholar]
- M. Krol and P. Osinski, “CNN-based gaze estimation and facial landmark recognition for screen engagement monitoring,” IEEE Trans. Affective Computing, vol. 10, no. 3, pp. 423–435, 2019. [Google Scholar]
- F. Alharbi, L. Huang, and T. Nguyen, “Multimodal screen usage monitoring using keystroke dynamics and facial recognition,” IEEE Access, vol. 7, pp. 112230–112244, 2019. [Google Scholar]
- A. Przybylski and N. Weinstein, “Digital screen time and psychological well-being: Evidence from large-scale studies,” Computers in Human Behavior, vol. 76, pp. 263–271, 2017. [Google Scholar]
- G. Mark, D. Gudith, and U. Klocke, “The cost of interrupted work: More speed and stress,” Proc. SIGCHI Conf. Human Factors in Computing Systems, pp. 107–110, 2008. [Google Scholar]
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, real-time object detection,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), pp. 779–788, 2016. [Google Scholar]
- Y. Zhang, L. Wang, and X. Hu, “YOLO-based workplace monitoring for real-time person detection,” IEEE Trans. Industrial Informatics, vol. 16, no. 12, pp. 7680–7688, 2020. [Google Scholar]
- J. Smith and A. Patel, “MySQL-driven activity logging system for long-term user productivity analysis,” IEEE Int. Conf. Smart Computing (SMARTCOMP), pp. 145–150, 2018. [Google Scholar]
- P. Gupta, S. Mehra, and V. Rao, “SQLite-based mobile parental control application with continuous user activity monitoring,” IEEE Int. Conf. Mobile Computing and Sustainable Informatics, pp. 331–336, 2021. [Google Scholar]
- R. Szeliski, Computer Vision: Algorithms and Applications, 2nd ed. London, U.K.: Springer, 2022. [Google Scholar]
- I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. Cambridge, MA: MIT Press, 2016. [Google Scholar]
- K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 2016, pp. 770–778. [Google Scholar]
- S. Patel, “Real-time human activity recognition using deep learning techniques,” M.S. thesis, Dept. Computer Science, Indian Institute of Technology (IIT), Bombay, India, 2021. [Google Scholar]
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