Development of Image Processing Technology in the Design of Smart Medicine Cabinets for Hospitals

Faculty of Electrical engineering, University of Science and Technology, The University of Danang Da Nang, Viet Nam This email address is being protected from spambots. You need JavaScript enabled to view it.
Faculty of Electrical engineering, University of Science and Technology, The University of Danang Da Nang, Viet Nam This email address is being protected from spambots. You need JavaScript enabled to view it.
Faculty of Electrical engineering, University of Science and Technology, The University of Danang Da Nang, Viet Nam This email address is being protected from spambots. You need JavaScript enabled to view it.
Faculty of Mechanical engineering, University of Science and Technology, The University of Danang Da Nang, Viet Nam This email address is being protected from spambots. You need JavaScript enabled to view it.
Faculty of Information Technology, University of Science and Technology, The University of Danang Da Nang, Viet Nam This email address is being protected from spambots. You need JavaScript enabled to view it.
Faculty of Project Management, University of Science and Technology, The University of Danang Da Nang, Viet Nam

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

In the context of increasing pressure on healthcare systems, the application of automation and artificial intelligence (AI) has become a key solution for optimizing healthcare delivery processes. This study proposes the design of a smart medicine cabinet system based on a microcontroller platform, such as STM32, integrated with IoT and AI technologies to automate medication dispensing. The system employs a camera and authentication algorithms, including facial recognition or PIN-based verification, in combination with image processing techniques to ensure that medication is delivered to the correct patient while also supporting the assessment of the patient’s current condition. Through appropriate model training, the cabinet is expected to identify the patient’s health status by analyzing visual features extracted from images, particularly skin-related characteristics. With a hardware architecture comprising servo motors for compartment control, an alarm speaker, and a display interface, the smart cabinet not only reminds patients to take their medication but also assists healthcare personnel in remotely managing treatment data via a mobile application. Experimental results indicate that the system operates stably, minimizes medication-related errors, reduces the distance between hospitals and patients, and is particularly beneficial for supporting elderly individuals and preventing cross-infection in hospital environments.