Call for Papers
About the Journal
Editorial Board
Publication Ethics
Instructions for Authors
Announcements
Current Issue
Back Issues
Search for Articles
Categories
Back Issues
 

JCSE, vol. 12, no. 4, pp.149-156, December, 2018

DOI: http://dx.doi.org/10.5626/JCSE.2018.12.4.149

CNN-Based Drug Recognition and Braille Embosser System for the Blind

Soyeong Lee, Sunhae Jung, and Hyunjoo Song
Department of IT Media Engineering, Duksung Women's University, Seoul, Korea

Abstract: Visual impairments reduce one's ability to perform daily tasks such as taking medicine. While the sighted can use their vision to effortlessly locate and identify drugs, the blind must rely on external assistance to complement their visual sense. Thus, receiving appropriate aid at the right time is crucial to avoid the misuse of drugs. We conducted interviews regarding medicine intake with 30 partially or completely blinded persons registered at three supporting facilities. Participants reported limitations of their current methods in finding their medication which led to them taking unintentional irregular doses caused by the lack of aid. Based on the results of the interview, we developed a drug recognition model and braille embosser system for Android smartphones. Using a picture of a medicine taken with a built-in camera, the CNN-based recognition model can classify 11 types of medicines with 99.6% accuracy. In addition, a low-cost braille embosser, which can connect to one's smartphone via Bluetooth, can print the classification results as a braille label for future identification without a smartphone.

Keyword: Human-computer interaction; Deep learning; Drug recognition; Braille embosser

Full Paper:   345 Downloads, 1267 View

 
 
ⓒ Copyright 2010 KIISE – All Rights Reserved.    
Korean Institute of Information Scientists and Engineers (KIISE)   #401 Meorijae Bldg., 984-1 Bangbae 3-dong, Seo-cho-gu, Seoul 137-849, Korea
Phone: +82-2-588-9240    Fax: +82-2-521-1352    Homepage: http://jcse.kiise.org    Email: office@kiise.org