@inproceedings{6b84f7ef080a4d28800ea341585b2d3e,
title = "Demo abstract: Mobile sensing to improve medication adherence",
abstract = "One of major challenges in chronic disease self-management is the lack of medication adherence. Despite the proliferation of mobile technologies, the potential of using pervasive computing solutions for improved medication management has remained almost unexplored. In this paper, we present a smart-phone based system capable of delivering adaptive activity-aware medication reminders by learning the user's activity of daily living and detecting the most appropriate and effective timing for medication reminders centered around the initial user-specified schedule.",
keywords = "Activity learning, Medication adherence, Prompting",
author = "Ramin Fallahzadeh and Bryan Minor and Evangelista, \{Lorraine S.\} and Cook, \{Diane J.\} and Hassan Ghasemzadeh",
note = "Funding Information: This work was supported in part by the National Institutes of Health under Grant No.: 1R21NR015410-01A1. Any opinions, findings, conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the funding organizations.; 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017 ; Conference date: 18-04-2017 Through 20-04-2017",
year = "2017",
month = apr,
day = "18",
doi = "10.1145/3055031.3055045",
language = "English (US)",
series = "Proceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017",
publisher = "Association for Computing Machinery, Inc",
pages = "279--280",
booktitle = "Proceedings - 2017 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, IPSN 2017",
}