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Research paper | Open Access

AI empowered context-aware smart system for medication adherence

Qiong Wu1( )Zhiwei Zeng2Jun Lin1Yiqiang Chen3
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Nanyang Technological University, Singapore, Singapore
Joint NTU-UBC Research Centre of Excellence in Active Living for the Elderly, Interdisciplinary Graduate School, Nanyang Technological University, Singapore, Singapore
Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China
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Abstract

Purpose

Poor medication adherence leads to high hospital admission rate and heavy amount of health-care cost. To cope with this problem, various electronic pillboxes have been proposed to improve the medication adherence rate. However, most of the existing electronic pillboxes use time-based reminders which may often lead to ineffective reminding if the reminders are triggered at inopportune moments, e.g. user is sleeping or eating.

Design/methodology/approach

In this paper, the authors propose an AI-empowered context-aware smart pillbox system. The pillbox system collects real-time sensor data from a smart home environment and analyzes the user’s contextual information through a computational abstract argumentation-based activity classifier.

Findings

Based on user’s different contextual states, the smart pillbox will generate reminders at appropriate time and on appropriate devices.

Originality/value

This paper presents a novel context-aware smart pillbox system that uses argumentation-based activity recognition and reminder generation.

References

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International Journal of Crowd Science
Pages 102-109

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Cite this article:
Wu Q, Zeng Z, Lin J, et al. AI empowered context-aware smart system for medication adherence. International Journal of Crowd Science, 2017, 1(2): 102-109. https://doi.org/10.1108/IJCS-07-2017-0006

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Received: 27 July 2017
Revised: 29 August 2017
Accepted: 30 August 2017
Published: 12 June 2017
© The author(s)

Qiong Wu, Zhiwei Zeng, Jun Lin and Yiqiang Chen. Published in International Journal of Crowd Science. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode