A Fully Automated Intelligent Medicine Dispensary System Based on AIoT

Jui-Hung Chang, Hoeyuan Ong, Tihao Wang, Hsiao Hwa Chen

研究成果: Article同行評審

摘要

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.

原文English
頁(從 - 到)1
頁數1
期刊IEEE Internet of Things Journal
DOIs
出版狀態Accepted/In press - 2022

All Science Journal Classification (ASJC) codes

  • 訊號處理
  • 資訊系統
  • 硬體和架構
  • 電腦科學應用
  • 電腦網路與通信

指紋

深入研究「A Fully Automated Intelligent Medicine Dispensary System Based on AIoT」主題。共同形成了獨特的指紋。

引用此