A development of medication assist device based on multi-object recognition

Yu Sheng Lin, Chia Ching Tsai, Kai Ming Chang, Pao Chin Shih, Ching Lan Cheng

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

Abstract

When the human population is experiencing a decline but the turnover rate of pharmacists in general hospitals is gradually increasing, department of pharmacy starts to import more modern technologies including automation and artificial intelligence to aid in the workflow. One of the lengthy and routine work is to count the number of remaining medications of each ward, which requires many pharmacists and technicians depends on the size of hospital. This study thereby introduces a design of a medication assist device with an integration of the machine vision and multiple object recognition algorithm. The work can be divided into hardware design, data collection, training and validation, respectively. The recognition algorithm is based on deep learning Faster RCNN, which can successfully identify 7 classes of the anesthetics often used with an accuracy of 99.03%. This pilot study presents the capability of medication recognition, and the potential to expand numbers of medication.

Original languageEnglish
Title of host publication2020 IEEE Region 10 Conference, TENCON 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages224-228
Number of pages5
ISBN (Electronic)9781728184555
DOIs
Publication statusPublished - 2020 Nov 16
Event2020 IEEE Region 10 Conference, TENCON 2020 - Virtual, Osaka, Japan
Duration: 2020 Nov 162020 Nov 19

Publication series

NameIEEE Region 10 Annual International Conference, Proceedings/TENCON
Volume2020-November
ISSN (Print)2159-3442
ISSN (Electronic)2159-3450

Conference

Conference2020 IEEE Region 10 Conference, TENCON 2020
CountryJapan
CityVirtual, Osaka
Period20-11-1620-11-19

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Electrical and Electronic Engineering

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