A Fully Automated Intelligent Medicine Dispensary System Based on AIoT

The COVID-19 pandemic has caused a high rate of infection, and thus effective epidemic prevention measures of avoiding the second spread of COVID-19 in hospitals are major challenges for healthcare workers. Hospitals, where medicines are collected, are vulnerable to the rapid spread of COVID-19. Usi...

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Bibliographic Details
Published in:IEEE internet of things journal 2022-12, Vol.9 (23), p.23954-23966
Main Authors: Chang, Juihung, Ong, Hoeyuan, Wang, Tihao, Chen, Hsiao-Hwa
Format: Article
Language:English
Subjects:
Applications programs
Artificial intelligence
Character recognition
Coronaviruses
COVID-19
Data recording
Health care
Hospitals
Internet of Things
Internet of Things (IoT)
Medical diagnostic imaging
Medical personnel
Medicine
medicine dispensary counter
Mobile computing
Monitoring
Optical character recognition
Optical character recognition software
optional character recognition
Patients
PP-OCR v2
Remote monitoring
Telemedicine
Viral diseases
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Summary:The COVID-19 pandemic has caused a high rate of infection, and thus effective epidemic prevention measures of avoiding the second spread of COVID-19 in hospitals are major challenges for healthcare workers. Hospitals, where medicines are collected, are vulnerable to the rapid spread of COVID-19. Using the remote health monitoring technology of the Internet of Things (IoT) to automatically monitor and record the basic medical information of patients, reduce the workload of healthcare workers, and avoid direct contact with healthcare workers to cause secondary infections is an important research topic. This research proposes a new artificial intelligence solution based on the IoT, replacing existing medicine stations and recognizing medicine bags through the state-of-the-art optical character recognition (OCR) model and PP-OCR v2. The use of OCR in identification of medicine bags can replace healthcare workers in data recording. In addition, this research proposes an administrator management and monitoring system to monitor the equipment and provide a mobile application for patients to check the latest status of medicine bags in real time, and record their medication times. The results of the experiments indicate that the recognition model works very well in different conditions (up to 80.76% in PP-OCR v2 and 94.22% in PGNet), which supports both Chinese and English languages.
ISSN:2327-4662
2327-4662
DOI:10.1109/JIOT.2022.3188552