TY - GEN
T1 - A development of medication assist device based on multi-object recognition
AU - Lin, Yu Sheng
AU - Tsai, Chia Ching
AU - Chang, Kai Ming
AU - Shih, Pao Chin
AU - Cheng, Ching Lan
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/11/16
Y1 - 2020/11/16
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85098948101&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85098948101&partnerID=8YFLogxK
U2 - 10.1109/TENCON50793.2020.9293874
DO - 10.1109/TENCON50793.2020.9293874
M3 - Conference contribution
AN - SCOPUS:85098948101
T3 - IEEE Region 10 Annual International Conference, Proceedings/TENCON
SP - 224
EP - 228
BT - 2020 IEEE Region 10 Conference, TENCON 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Region 10 Conference, TENCON 2020
Y2 - 16 November 2020 through 19 November 2020
ER -