Patient identification using a smart pill-bottle: Poster abstract

Murtadha Aldeer, Joseph Florentine, Jakub Kolodziejski, Jorge Ortiz, Richard E. Howard, Richard P. Martin

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

1 Scopus citations

Abstract

In this work, we investigate the identification of persons taking medication using a sensor-equipped pill-bottle. The bottle embeds inertial sensors in both the cap and body, making the added hardware un-obtrusive, low-cost, and wireless. Our system uses inertial data to build a patient discrimination model using classification techniques. We evaluated the system using 16 subjects. Our results show that using binary Support Vector Machine (SVM), the system can discriminate one patient among 16 subjects with 94 % accuracy. Identifying the exact person in a set of 3 subjects has an accuracy higher than 91 %..

Original languageEnglish (US)
Title of host publicationSenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems
EditorsMi Zhang
PublisherAssociation for Computing Machinery, Inc
Pages424-425
Number of pages2
ISBN (Electronic)9781450369503
DOIs
StatePublished - Nov 10 2019
Event17th ACM Conference on Embedded Networked Sensor Systems, SenSys 2019 - New York, United States
Duration: Nov 10 2019Nov 13 2019

Publication series

NameSenSys 2019 - Proceedings of the 17th Conference on Embedded Networked Sensor Systems

Conference

Conference17th ACM Conference on Embedded Networked Sensor Systems, SenSys 2019
Country/TerritoryUnited States
CityNew York
Period11/10/1911/13/19

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

Keywords

  • Clustering
  • DTW
  • SVM
  • Smart pill Bottle
  • User discrimination

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