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Hosung Kwon

dblp:325/1574 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1 · 1 since 2021Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Human-computer interaction and pervasive computing
1 paper
Health and well-being technologies · 100%
Artificial intelligence
1 paper
Face, body and person analysis · 100%

Topics — the 2 heaviest of 2, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Health and well-being technologies › chronic illness management
medication management
0.612022
PillGood: Automated and Interactive Pill Dispenser Using Facial Recognition for Safe and Personalized Medication · IJCAI 2022
Computer vision › Face, body and person analysis
face recognition
0.212022
PillGood: Automated and Interactive Pill Dispenser Using Facial Recognition for Safe and Personalized Medication · IJCAI 2022

Methods — techniques the papers use, named apart from their topics

machine learning · 1.1face recognition · 1.1
YearPublicationVenuePosition
2022 PillGood: Automated and Interactive Pill Dispenser Using Facial Recognition for Safe and Personalized Medication
abstract
Safety of taking medicine prescribed differently to each patient in hospital relies on the discernment of medical professionals who deals with measuring pill quantity, packaging, and distributing. It is difficult and time consuming to keep track of medication record of each patient. Also, medication safety is prone to be in risk due to the human error. To help patients get accurate medication following their prescription plan with minimizing human labors and mistakes, we developed PillGood, an automated smart pill dispenser system using facial recognition technique. PillGood provides real-time and personalized guidance to take the correct medicine by alarming patients and distributing exact quantity of pills at specific time following each patient's prescription table. The system notify patients through mobile app and speaker when they need to take the medicine, and detect who the patient is through the machine learning based face recognition. Then, based on each patient's prescribing information, the controller distributes pills to each patient. Results show that PillGood enable highly accurate personalized pill dispensation followed by precise face recognition, benefiting both patients and medical professionals. Videos for demonstrating the system can be found on https://youtu.be/Wx7bXxRGjXA
Jonghyeok Kim, Hosung Kwon, Jonghyeon Kim, Soong-Un Choi, Sookyung Kim
IJCAI2