PillGood: Automated and Interactive Pill Dispenser Using Facial Recognition for Safe and Personalized Medication

PillGood: Automated and Interactive Pill Dispenser Using Facial Recognition for Safe and Personalized Medication

Jonghyeok Kim, Hosung Kwon, Jonghyeon Kim, Jinsoo Park, Soong-Un Choi, Sookyung Kim

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence
Demo Track. Pages 5920-5923. https://doi.org/10.24963/ijcai.2022/854

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
Keywords:
Multidisciplinary Topics and Applications: AI Hardware
Computer Vision: Applications
Computer Vision: Recognition (object detection, categorization)
Humans and AI: Applications
Multidisciplinary Topics and Applications: Health and Medicine