Amulet paper at UbiTtention/Ubicomp

Abstract: In this work, we attempt to determine whether the contextual information of a participant can be used to predict whether the participant will respond to a particular EMA trigger. We use a publicly available dataset for our work, and find that by using basic contextual features about the participant’s activity, conversation status, audio, and location, we can predict if an EMA triggered at a particular time will be answered with a precision of 0.647, which is significantly higher than a baseline precision of 0.41. Using this knowledge, the researchers conducting field studies can efficiently schedule EMAs and achieve higher response rates.

Varun Mishra, Byron Lowens, Sarah Lord, Kelly Caine, and David Kotz. Investigating Contextual Cues As Indicators for EMA Delivery. In Proceedings of the International Workshop on Smart & Ambient Notification and Attention Management (UbiTtention), pages 935-940, September 2017. ACM. DOI 10.1145/3123024.3124571.

This entry was posted in Publications and tagged , , , , by David Kotz. Bookmark the permalink.

About David Kotz

David Kotz is the Pat and John Rosenwald Professor in the Department of Computer Science at Dartmouth College. He previously served as Interim Provost, as Associate Dean of the Faculty for the Sciences, as the Executive Director of the Institute for Security Technology Studies, and on the US Healthcare IT Policy Committee. His research interests include security and privacy, pervasive computing for healthcare, and wireless networks. He has published over 230 refereed papers, obtained over $80m in grant funding, and mentored nearly 100 research students. He is a Fellow of the IEEE, a Distinguished Member of the ACM, a 2008 Fulbright Fellow to India, a 2019 Visiting Professor at ETH Zurich, and an elected member of Phi Beta Kappa. He received his AB in Computer Science and Physics from Dartmouth in 1986, and his PhD in Computer Science from Duke University in 1991.

Leave a comment

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s