Killing-Time Detection from Smartphone Screenshots

Yu Chun Chen, Keui Chun Kao, Yu Jen Lee, Faye Shih, Wei Chen Chiu, Yung Ju Chang

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

Finding good moments to deliver interruptions has drawn research attention. Since users have attention surplus at these moments, killing-Time is considered one such a kind of moment. However, detection on killing-Time has been under researched. In this paper, we propose a screenshot-based killing-Time detection with deep learning. Our model achieves an accuracy 79.71%, recall 90.24%, precision 84.51%, and AUROC 65.50%. This suggests that using screenshots to detect users' kill time behavior on smartphones is a promising approach. It may be worthwhile to investigate how the fusion of screenshots and sensor data can further improve detection.

原文English
主出版物標題UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers
發行者Association for Computing Machinery, Inc
頁面15-16
頁數2
ISBN(電子)9781450384612
DOIs
出版狀態Published - 21 9月 2021
事件2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2021 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2021 - Virtual, Online, United States
持續時間: 21 9月 202125 9月 2021

出版系列

名字UbiComp/ISWC 2021 - Adjunct Proceedings of the 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2021 ACM International Symposium on Wearable Computers

Conference

Conference2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2021 ACM International Symposium on Wearable Computers, UbiComp/ISWC 2021
國家/地區United States
城市Virtual, Online
期間21/09/2125/09/21

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