Detection of Gestures Associated with Medication Adherence Using Smartwatch-Based Inertial Sensors

Haik Kalantarian, Nabil Alshurafa, Majid Sarrafzadeh

Research output: Contribution to journalArticle

22 Scopus citations

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 several motions that may be predictors of medication adherence, using built-in triaxial accelerometers and gyroscopes. The efficacy of the proposed technique is confirmed through a survey of medication ingestion habits and experimental results on movement classification.

Original languageEnglish (US)
Article number7315013
Pages (from-to)1054-1061
Number of pages8
JournalIEEE Sensors Journal
Volume16
Issue number4
DOIs
StatePublished - Feb 15 2016

Keywords

  • Gesture recognition
  • measurement/accelerometers
  • pervasive computing
  • wearable computers

ASJC Scopus subject areas

  • Instrumentation
  • Electrical and Electronic Engineering

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