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.

Highlights

  • With rapid population aging and rising health-care cost, ensuring medication adherence is becoming increasingly important

  • Most of the existing electronic pillboxes adopt time-based reminders, which may not successfully improve adherence if the time-triggered alerts occur at inopportune moments

  • Incorporating context awareness into the reminders of smart pillboxes could increase the chance of successful medication adherence by alerting at opportune moments

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Summary

Introduction

With rapid population aging and rising health-care cost, ensuring medication adherence is becoming increasingly important. A reminder is triggered when the user is eating, whereas the medication should be taken after meal. Incorporating context awareness into the reminders of smart pillboxes could increase the chance of successful medication adherence by alerting at opportune moments. A novel argumentation based approach is adopted for solving the activity recognition problem (Fan et al, 2016) and reminder planning problem (Zeng et al, 2017).

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