• IEMEK Journal of Embedded Systems and Applications
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• v.10 no.1
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• pp.13-23
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• 2015

Recently, a few image-processing based mobile urine testers have actively been studied since the urine-analysis result can be available to the user in real time immediately after the test is done. However, the accuracy of test result can be severely degraded due to variable illumination environments and a variety of manners to capture the image with a camera embedded in the smartphone according to different users. This paper proposes the Check4Urine system, a novel smartphone-based portable urine-analysis tester and provides three techniques to improve such a performance degradation problem robust to various test environments and disturbances, which are the compensation algorithm to correct the varying illumination effect, an urine strip detection algorithm robust to edge loss of the object image, and the color decision algorithm based on the pre-processed reference table. Experimental results show that the proposed Check4Urine system increases the accuracy of urine-analysis by 20-50% at various test conditions, compared with the existing image-processing based mobile urine tester.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.10
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• pp.1394-1397
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• 2020

The urine test used as a basic test method of in vitro diagnosis for health care has been used for a long time to be simple and convenient. The urine test method is using a color that appears depending on the change in the ion concentration that reacts over time buried in the standard color test paper(Strips) with a urine sample applied to some reaction reagents. In this paper, it was proposed a neural network algorithm to obtain a suitable and reproducibility and accuracy classifier suitable for the urine analysis system. The experimental results were compared with the visual colorimetric analysis, and the neural network algorithm showed better results.

• Applied Chemistry for Engineering
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• v.5 no.6
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• pp.975-980
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• 1994

The preparation procedure and optimal composition of polyurethane diagnostic membranes were described to measure the glucose concentration in a urine. Vessel size, blade size and the ratio of solvent mixtures were found to be critical factors to get the better sensitivity and the stability of the color which appeared on polyurethane membranes after the reaction with the urine glucose. These urine strips made of polyurethane membranes made it possible to measure the urine glucose quantitatively because they showed a good color separation at glucose concentration from 30mg/dL to 500mg/dL.

• Journal of Korean Biological Nursing Science
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• v.14 no.2
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• pp.139-146
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• 2012

Purpose: This study was done to evaluate the effects of a fluid intake intervention on increasing fluid intake and ameliorating dehydration status in elders admitted to long-term care hospitals. Methods: A nonequivalent control group, pretest and posttest design was used. The experimental group of 39 participants received the 4-week intervention while the control group of 38 participants received routine care. Outcome variables were daily fluid intake and physiological indexes such as blood urea nitrogen and creatinine ratio (BUN/Cr), urine specific gravity (USG), and urine color. Results: After the intervention to increase fluid intake, there were statistically significant increases in daily fluid intake, normal BUN/Cr, and USG in the experimental group. However, a statistically significant improvement in normal urine color was not found for either group. Conclusion: The findings of this study demonstrated that the fluid intake intervention improved hydration status of the experimental group participants. Consequently, it was confirmed that the intervention is considered to be effective in preventing dehydration which occurs frequently in older adults in long-term care facilities and, thus this intervention may contribute to preventing various health issues resulting from dehydration.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.10 no.7
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• pp.1319-1325
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• 2006

Urine analysis is basic test in clinical medicine using visual examination by expert nurse. Recently, this test is measured by automatic urine analysis system. But, this system has different results by each instrument. So, a new classification algorithm is required for accurate classify and urine color collection. In this paper, a intelligent color classifier of urine analysis system was designed using neural network algorithm. The input parameters are three stimulus(RGB) after preprocessing using normalization. The fuzzy inference and neural network ware constructed for classify class according to 9 urine test items and $3{\sim}7$ classes. The experiment material to be used a standard sample of medicine. The possibility to adapt classifier designed for urine analysis system was verified as classifying measured standard samples and observing classified result. Of many test items, experimental results showed a satisfactory agreement with test results of reference system.

• Biomedical Science Letters
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• v.2 no.2
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• pp.199-209
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• 1996

A new test strip to detect leukocytes using the myeloperoxidase in urine was developed. The reagent strip contains tetramethylbenzine, glucose and glucose oxidase. The detection limit was between 10 cells per 1$\mu$l urine(5 cells/hpf), showing greenish yellow color in the range of 10-25 cells/$\mu$l, green color in the range of 75-250 cells/$\mu$l, greenish blue color in the range of 500 cells/$\mu$l. The result can be obtained within two minute. The performance of the new method was evaluated by comparing the results of microscopic examination and other commercial products. Good correlations were shown between the values obtained by our urine strip and those by other commercial products with 172 urine samples. The results were proven that new methods were useful as primary screening reagents to detect leukocytes in urine.

• Journal of the Korea Convergence Society
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• v.12 no.5
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• pp.93-99
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• 2021

Recently, the miniaturization and digitalization for the inspection devices of point-of-care testing (POCT) are rapidly evolving. In the urine test, a lot of researches on index paper technology are being conducted because people can be self-diagnosed through visual color comparison using a urine test paper, Dipsick. The purpose of this study is to analyze the RGB values from the color changes on Dipstick Pad, which isused for urine test, using a smartphone camera. To this end, the primary, analytes in urine wasdiabetes-related parameters such as glucose, ketone body and pH, which is the most frequently tested elements, and we pursuited to quantify the changes in dipstick color caused from artificial urine containing different ranges of sugar, ketone body, and pH. In this experiment, changes in RGB values under bright and dark illuminances were compared, and changes in RGB value were monitored as a function of concentration of analytes under the ambient illumination of laboratory. As a result, color separation at the bright luminance region was good, but it did not appearat the low luminance region, and the changed profiles in RGB value under different illuminances was suggested to correct the problem of the color separation algorithm.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.23 no.4
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• pp.41-46
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• 2023

Human urine is a process of excreting waste products in the blood, and it is easy to collect and contains various substances. Urinalysis is used to check for diseases, health conditions, and urinary tract infections. There are three methods of urinalysis: physical property test, chemical test, and microscopic test, and chemical test results can be easily confirmed using urine test strips. A variety of items can be tested on the urine test strip, through which various diseases can be identified. Recently, with the spread of smart phones, research on reading urine test strips using smart phones is being conducted. There is a method of detecting and reading the color change of a urine test strip using a smartphone. This method uses the RGB values and the color difference formula to discriminate. However, there is a problem in that accuracy is lowered due to various environmental factors. This paper applies a deep learning model to solve this problem. In particular, color discrimination of a urine test strip is improved in a smartphone using a lightweight CNN (Convolutional Neural Networks) model. CNN is a useful model for image recognition and pattern finding, and a lightweight version is also available. Through this, it is possible to operate a deep learning model on a smartphone and extract accurate urine test results. Urine test strips were taken in various environments to prepare deep learning model training images, and a urine test service application was designed using MobileNet V3.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2001.11a
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• pp.328-331
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• 2001

Color simulation on a portable colorimeter was performed to distinguish quantitatively a chromaticity coordinates on a color guide of a urine strips by using the spectral power distribution of chip LED, the spectral reflectance of printed objects, and the spectral sensitivity of photodiode. The CIE tristimulus values and chromaticity coordinates realized by a colorimeter were modified to be conformable with real color reactions. Experimental results showed a real color in comparison with those obtained by Colorimeter CM2C(Color Savvy).

• Archives of Pharmacal Research
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• v.16 no.3
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• pp.227-230
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• 1993

A new device to detect methamphetamine (MA), amphetamine(A) and its metabolites in urine was developed using the paper strip method and the test tube method of dry chemical reagents. The reagent containing tetrabromophenolphthalein ethyl ester (TBPE) and borax. For the TBPE paper strip method, a device was prepared with a window at each end of the reagent paper strip ; one window is for the sample application, and the other window is for the methylene chloride. The diffused sample from one window reacts with reagent in the paper and produces color at the point where it meets with methylene chloride which has diffused form the other side. A positive smaple produces as red-purple color and the negative sample a greenish color, with a detection limit of 5-10 ppm. The result can be obtained within one minute. For the TBPE test tube method which contains dry reagents, the detection limit is 5 ppm and the result can be obtaineed within 30 seconds, however the carry-on is not as convenient as the paper strip method. The performance of both methods were evlauated by comparing with the results of gas chromatography (GC) and fluorescence polarizaiton immunoassay (FPIA). The results were proven that both methods were useful as primary screening reagents to detect MA in urine and in dry powder.