A Fully Automated Intelligent Medicine Dispensary System Based on AIoT
IEEE Internet of Things Journal
; : 1-1, 2022.
Article
in English
| Scopus | ID: covidwho-1961407
ABSTRACT
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 optical character recognition 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. IEEE
Character recognition; COVID-19; Hospitals; Internet of Things; Medical diagnostic imaging; Medicine Dispensary Counter; Monitoring; Optical character recognition software; Optional Character Recognition; PP-OCR v2; Diagnosis; Health care; Medical imaging; Optical character recognition; Fully automated; Healthcare workers; High rate; PP-optical character recognition v2; Rate of infections; Recognition models
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Language:
English
Journal:
IEEE Internet of Things Journal
Year:
2022
Document Type:
Article
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