Demo Data from SIGIR 2026 / reviewers in the wild / expert
Hosung Kwon
dblp:325/1574
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Health and well-being technologies › chronic illness management
medication management |
0.6 | 1 | 2022 | 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 |
0.2 | 1 | 2022 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2022 | PillGood: Automated and Interactive Pill Dispenser Using Facial Recognition for Safe and Personalized MedicationabstractSafety 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 |
IJCAI | 2 |