• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2013.10a
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• pp.1010-1011
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• 2013

In this paper, we have developed a system for identifying pills and providing their information by image processing to assist the health care of the elderly. A wide variety of pills can lead to the difficulty of distinguishing for the elderly at home and results in unusage and abandonment of the pills. To resolve this problem, we obtain images from a camera of a smart phone, extract features such as shape, line and color by image processing, classify pills, and provide information including the name, effect and directions for the usage of the pills.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.24 no.1
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• pp.9-16
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• 2024

It is difficult to know the efficacy of pills if the pill bag or wrapper is lost after purchasing the pill. Many people do not classify the use of commercial pills when storing them after purchasing and taking them, so the inaccessibility of information on the side effects of pills leads to misuse of pills. Even with existing applications that search and provide information about pills, users have to select the details of the pills themselves. In this paper, we develope a pill recognition application by building a model that learns the formulation and colour of 22,000 photos of pills provided by a Pharmaceutical Information Institution to solve the above situation. We also develope a pill medication management function.

• Journal of the Korea Society of Computer and Information
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• v.19 no.2
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• pp.49-56
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• 2014

This thesis proposes CBIRS/TB method that uses a tablet's color distribution information and form distinctive in content-based search. CBIRS/TB can avoid misuses and improper tablet uses by conducting content-based search in commonly prescribed tablets. The existing FE-CBIRS system is limited to recognizing only the image of color and shape of the tablet, that leads to applying insufficient form-specific information. While CBIRS/TB utilizes average, standard deviation, hue and saturation of each tablets in color, brightness, and contrast, FE-CBIRS has partial-sphere application problem; only applying the typical color of the tablet. Also, in case of the shape-specific-information, Invariant Moment is mainly used for the extracted partial-spheres. This causes delayed processing time and accuracy problems. Therefore, to improve this setback, this thesis indexed color-specific-information of the extracted images into categorized classification for improved search speed and accuracy.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.854-856
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• 2016

Nowadays, the interests about medical service and health concerting, in other words "Healthcare",are increasing. For healthcare, one of the most convenient and populated methods is taking nutritional supplements like vitamin everyday. According to social currency, we realize ' Automatic Pill Dispenser (APD)' in order to make the storage of pills easy and to help people take pills steadily periodically. First, the APD alerts at the time when the user sets. To prevent polluting pills, if the ultrasonic sensor recognizes user hands, and then after the APD distributes pills through activating the motor. If there are more the APDs, for example, hospitals can manage manpower more efficiently. Furthermore, If it is recorded which and how much pills are distributed automatically, It can be expected that people can take the pills more efficiently and healthfully.

• Proceedings of the Korea Information Processing Society Conference
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• 2024.05a
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• pp.13-14
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• 2024

최근 의약품 복용량의 급증으로, 효과적인 복약 관리가 중요해졌다. 의약품 복약이 제 시간에 이루지지않거나 꾸준히 이루어지지 않는 경우 효과적인 약효를 기대하기 어렵고 부작용 발생 가능성이 증가할 수 있기 때문이다. 따라서 본 연구는 AI 를 활용한 알약 인식 서비스를 통해 사용자의 편의성을 높이고, 자동 복용 알림을 제공하여 올바른 복용 습관을 장려할 수 있는 모바일 스케줄러를 개발하였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2020.11a
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• pp.889-892
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• 2020

현재 식품의약품안전처에서 공공데이터 포털에 제공하는 정보에 의하면 국내에는 20,000종 이상의 약이 유통되고 있다. 식약처와 여러 제약회사에서 기본적인 약 정보를 제공하고는 있지만 정확한 처방전이나 설명서가 없는 경우에 무분별한 약 복용의 위험성을 안고 있다. 일부 약 검색을 지원하는 사이트가 있으나 세부 사항을 사용자가 일일이 선택하고 입력해야 정확한 정보를 얻을 수 있다. 본 논문에서는 사용자의 스마트폰을 이용하여 알약을 촬영하면 해당 약을 인식하고 상세 정보를 알려주는 딥러닝 모델을 설계하였다. CNN 신경망을 사용하여 약의 모양, 색상, 마크, 분할선 등을 기준으로 분류하고 인식된 약의 세부 정보는 공공데이터로부터 받아온다.

• Proceedings of the Korean Society of Computer Information Conference
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• 2021.07a
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• pp.339-340
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• 2021

한 첩씩 복용하는 알약은 복용 여부를 정확히 판별하기 어렵다. 그래서 이러한 상황에서 벗어나고자 만든 스마트 약통을 제안하고 있다. 이 스마트 약통은 약의 오남용을 방지하고, 날짜별 복용 여부를 휴대폰 어플로 알려주는 장점을 가지고 있다. 장기간 복용하는 약은 한 번의 내원으로 많은 양의 약을 처방받아 오기 때문에 기억력이 좋지 않은 어른이 사용하기에 유용하다. 처방 받은 약통에 있는 QR코드를 최초 입력함으써 약 3일 정도의 데이터 수집기간을 통해 평균 복용시간을 인식하고, 평균 복용시간을 인식한 후에 약 먹을 시간을 알려주는 기능을 탑재하였다. 평상시에는 잠금장치를 통해 걸어 열 수 없게 프로그램을 설정하고, 복용시간에만 잠금장치를 해제하여 환자의 약물 오남용을 막고 안전하게 복용할 수 있을 것이다.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.9-9
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• 2000

Accurate counting and packaging pills is one of the most fundamental works of the pharmaceutical industry. But it is so labor consuming and very hard to be automated. As the pharmaceutical industry is growing bigger, the need of counting and packaging automation is increasing to obtain effective mass production. Precise and quick sensing is required in the counting and processing of quickly dropping pills to improve the productivity. There are many trials for this automation and automatic machine. But the performance of the existing counting machine varies with the size, shape and the dispersion degree of pills In this research, authors design the counting and packing machine of medicinal pills that is more accurate and highly trustworthy After getting analog signal from optical sensor, pill passage is discriminated from chosen characteristic feature using microprocessor.

• Journal of Korea Multimedia Society
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• v.22 no.3
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• pp.349-356
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• 2019

When a prescription change occurs in the hospital depending on a patient's improvement status, pharmacists directly classify manually returned pills which are not taken by a patient. There are hundreds of kinds of pills to classify. Because it is manual, mistakes can occur and which can lead to medical accidents. In this study, we have compared YOLO, Faster R-CNN and RetinaNet to classify and detect pills. The data consisted of 10 classes and used 100 images per class. To evaluate the performance of each model, we used cross-validation. As a result, the YOLO Model had sensitivity of 91.05%, FPs/image of 0.0507. The Faster R-CNN's sensitivity was 99.6% and FPs/image was 0.0089. The RetinaNet showed sensitivity of 98.31% and FPs/image of 0.0119. Faster RCNN showed the best performance among these three models tested. Thus, the most appropriate model for classifying pills among the three models is the Faster R-CNN with the most accurate detection and classification results and a low FP/image.