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http://dx.doi.org/10.6109/jkiice.2020.24.5.584

Performance Comparison of Machine Learning Models to Detect Screen Use and Devices  

Hwang, Sangwon (Department of Computer Engineering, Graduate School, KOREATECH)
Kim, Dongwoo (Department of Computer Engineering, Graduate School, KOREATECH)
Lee, Juhwan (Department of Computer Engineering, Graduate School, KOREATECH)
Kang, Seungwoo (School of Computer Science and Engineering, KOREATECH)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.24, no.5, 2020 , pp. 584-590 More about this Journal
Abstract
Long-term use of digital screens in daily life can lead to computer vision syndrome including symptoms such as eye strain, dry eyes, and headaches. To prevent computer vision syndrome, it is important to limit screen usage time and take frequent breaks. There are a variety of applications that can help users know the screen usage time. However, these apps are limited because users see various screens such as desktops, laptops, and tablets as well as smartphone screens. In this paper, we propose and evaluate machine learning-based models that detect the screen device in use using color, IMU and lidar sensor data. Our evaluation shows that neural network-based models show relatively high F1 scores compared to traditional machine learning models. Among neural network-based models, the MLP and CNN-based models have higher scores than the LSTM-based model. The RF model shows the best result among the traditional machine learning models, followed by the SVM model.
Keywords
Computer Vision Syndrome; Screen Use Detection; Screen Device Classification; Machine Learning;
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