• Journal of Institute of Control, Robotics and Systems
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• v.11 no.9
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• pp.810-815
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• 2005

As the pharmaceutical industry grows and becomes more competitive, the need of automation increases to establish effective mass production and to maintain consistent quality control. Accurate counting and packing of medicinal pills is one of the most essential processes that the automation can benefit. In conventional automated counting and packing processes, the performance of counting process varies with the size, the shape and the dispersion degree of pills. In this research, the authors developed a new pill-counting algorithm based on carefully analyzed characteristics of the pill-drop behavior. Also a new scheme for the packing of an exact number of pills has been implemented. A pill counting and packing machine with the new pill-counting algorithm and the new packing scheme has been constructed and put in an actual production line. To achieve precise and quick sensing of pills dropping at a high speed from the preceding processors, the machine uses non-contact photo sensors. Experimental results from the actual process using the machine are included to verify the effectiveness of the proposed algorithm and the machine.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.683-686
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• 2002

It is fundamental to count and pack the pills in the medicine manufacture field but those tasks are time and labor consuming. Thus, the need fur automation of those tasks is necessarily getting increased in order to get effective mass production. It Is significant to perceive pills quickly and precisely. There were many trials for this processing but the performance of the existing counting machines varies about size, shape and dispersion tendency of pills. In this paper, the authors try to improve the counting performance of a pill counting machine that has optical sensors with the neural network. The passing signal of pill is acquired with optical sensor and the passage signal of the pill is extracted as input patterns. The gradient and integration of signal during passing time and the time keeping the pill interrupt the light from the LED are used as characteristic feature. The back propagation and perception algorithm are used for training. Experimental results with several pills show that the designed algorithm is a little bit effective to reduce the noise effect which is generated from interference among the machine components and unreliable environment.

• 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.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.65 no.7
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• pp.1257-1262
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• 2016

The purpose of this study is to evaluate whether to take a medicine based on a measuring data using textile proximity sensor. We developed a proximity sensor of ring type using conductive textile, and acquired a data in accordance with the quantity of each pills. To evaluate our approach, we designed an experimental protocol that is counting pills subtracting the one which contains range of 0 T(Tablet, 4,100mg) from 20 T. And, The experiments were performed a nine times in the same way. In order to remove a noise and smoothen data, data preprocessing were performed using resampling method and moving average filter which has ten points. Then, we calculated a linear trend line equation, and analyzed a correlation between pill quantity and trend line equation. As a results, correlation coefficient was shown at 0.833 through using a Spearman's correlation method and we could be determined that data was continuos decreases when take a medicine.

• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.55-63
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• 2013

Purpose: The present study aimed at image processing methods for automatic cell pattern recognition and morphological analysis for tissue engineering applications. The primary aim was to ascertain the novel algorithm of adaptive brightness correction from microscopic images for use as a potential image analysis. Methods: General microscopic image of cells has a minor problem which the central area is brighter than edge-area because of the light source. This may affect serious problems to threshold process for cell-number counting or cell pattern recognition. In order to compensate the problem, we processed to find the central point of brightness and give less weight-value as the distance to centroid. Results: The results presented that microscopic images through the brightness correction were performed clearer than those without brightness compensation. And the classification of mixed cells was performed as well, which is expected to be completed with pattern recognition later. Beside each detection ratio of hBMSCs and HeLa cells was 95% and 92%, respectively. Conclusions: Using this novel algorithm of adaptive brightness correction could control the easier approach to cell pattern recognition and counting cell numbers.

• Research in Community and Public Health Nursing
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• v.22 no.3
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• pp.290-301
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• 2011

Purpose: This study is a descriptive research intended to clarify the medication status of community-dwelling elders and to identify factors affecting their medication adherence. Methods: Data were collected using questionnaires and interviews from 101 subjects who had taken prescribed drugs for at least 7 days sampled among elderly people using home care nursing at a general hospital in Seoul. Results: According to the results of this study, medication adherence measured by pill counting was 88.3% and that measured by self-reporting was 94.6%. There were statistically significant differences in medication adherence according to major disease (p=.006), the number of admissions (p=.032), the number of drugs (p=.051), the frequency of medication (p=.026), and depression (r=-.205). In addition, depression was found to be a significant variable explaining the medication adherence with explanatory power 3.8% (p=.035). Conclusion: The presence of depression affected the elderly subjects' the medication adherence. Therefore, more concern and educational approaches are required to encourage elderly people to comply correctly with medication regimens particularly for elderly patients who have a malignant or long-lasting disease or who have to take multiple drugs or maintain a daily dosing frequency.