Poster Abstract: User identification across multiple smart pill bottle systems

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

1 Scopus citations

Abstract

Medication adherence is one of the leading factors that can make the difference between life and death, especially for patients managing chronic conditions [2]. Indeed, these issues have driven a recent wave of research, including the development of smart pill bottles that monitor when a pill is extracted. In this poster, we extend our recent work [1], where we present adaptive learning techniques for subject identification across multiple pill bottle systems. We collect inertial signals from 10 subjects taking medication pills and encode the activity signals by transforming them into 2D texture images. Then we use pre-trained Convolutional Neural Network (CNN) models for image-based classification tasks. Our approach achieved improved differentiation capacity over existing models by using deep learning models, modified through domain adaptation and transfer learning.

Original languageEnglish (US)
Title of host publicationProceedings of the 20th International Conference on Information Processing in Sensor Networks, IPSN 2021 (co-located with CPS-IoT Week 2021)
PublisherAssociation for Computing Machinery, Inc
Pages400-401
Number of pages2
ISBN (Electronic)9781450380980
DOIs
StatePublished - May 18 2021
Event20th International Conference on Information Processing in Sensor Networks, IPSN 2021, co-located with CPS-IoT Week 2021 - Virtual, Online, United States
Duration: May 18 2021May 21 2021

Publication series

NameProceedings of the 20th International Conference on Information Processing in Sensor Networks, IPSN 2021 (co-located with CPS-IoT Week 2021)

Conference

Conference20th International Conference on Information Processing in Sensor Networks, IPSN 2021, co-located with CPS-IoT Week 2021
Country/TerritoryUnited States
CityVirtual, Online
Period5/18/215/21/21

All Science Journal Classification (ASJC) codes

  • Information Systems
  • Computer Networks and Communications

Keywords

  • Deep learning
  • Inertial sensors
  • Smart pill bottles

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