@inproceedings{5d0a52cdbf8c446fa7419755153dc6b3,
title = "A smartwatch-based medication adherence system",
abstract = "Poor adherence to prescription medication can compromise treatment effectiveness and cost the billions of dollars in unnecessary health care expenses. Though various interventions have been proposed for estimating adherence rates, few have been shown to be effective. Digital systems are capable of estimating adherence without extensive user involvement and can potentially provide higher accuracy with lower user burden than manual methods. In this paper, we propose a smartwatch-based system for detecting adherence to prescription medication based the identification of several motions using the built-in tri-axial accelerometers and gyroscopes. The efficacy of the proposed technique is confirmed through a survey of medication ingestion habits and experimental results on movement classification.",
author = "Haik Kalantarian and Nabil Alshurafa and Ebrahim Nemati and Tuan Le and Majid Sarrafzadeh",
year = "2015",
month = oct,
day = "15",
doi = "10.1109/BSN.2015.7299348",
language = "English (US)",
series = "2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2015 IEEE 12th International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015",
address = "United States",
note = "12th IEEE International Conference on Wearable and Implantable Body Sensor Networks, BSN 2015 ; Conference date: 09-06-2015 Through 12-06-2015",
}