A machine learning approach for medication adherence monitoring using body-worn sensors

Niloofar Hezarjaribi, Ramin Fallahzadeh, Hassan Ghasemzadeh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Scopus citations

Abstract

One of the most important challenges in chronic disease self-management is medication non-adherence, which has irrevocable outcomes. Although many technologies have been developed for medication adherence monitoring, the reliability and cost-effectiveness of these approaches are not well understood to date. This paper presents a medication adherence monitoring system by user-activity tracking based on wrist-band wearable sensors. We develop machine learning algorithms that track wrist motions in real-time and identify medication intake activities. We propose a novel data analysis pipeline to reliably detect medication adherence by examining single-wrist motions. Our system achieves an accuracy of 78.3% in adherence detection without need for medication pillboxes and with only one sensor worn on either of the wrists. The accuracy of our algorithm is only 7.9% lower than a system with two sensors that track motions of both wrists.

Original languageEnglish (US)
Title of host publicationProceedings of the 2016 Design, Automation and Test in Europe Conference and Exhibition, DATE 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages842-845
Number of pages4
ISBN (Electronic)9783981537062
DOIs
StatePublished - Apr 25 2016
Externally publishedYes
Event19th Design, Automation and Test in Europe Conference and Exhibition, DATE 2016 - Dresden, Germany
Duration: Mar 14 2016Mar 18 2016

Publication series

NameProceedings of the 2016 Design, Automation and Test in Europe Conference and Exhibition, DATE 2016

Other

Other19th Design, Automation and Test in Europe Conference and Exhibition, DATE 2016
Country/TerritoryGermany
CityDresden
Period3/14/163/18/16

ASJC Scopus subject areas

  • Hardware and Architecture
  • Safety, Risk, Reliability and Quality

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