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The need for an economical Blood Group measuring solution is widespread, and it is especially critical in developing nations. The most widely used approach in both resource-rich and resource-limited environments is image processing, which would be an appropriate choice for this solution. In Invasive method to determine the blood type the patient blood sample is collected by pricking the fingertip of the patient, it may cause infection and pain to the patient. The aim of the proposed system is to provide a non-invasive Blood Group measurement processes using image processing and machine learning algorithm. In order to compute blood groups, it is also examined how different data collecting locations, bio signal processing methods, theoretical underpinnings, the photoplethysmogram (PPG) signal and feature extraction procedure, image processing algorithms, and detection models differ. The results of this study were then utilized to suggest practical methods for developing a non-invasive point-of-care tool for Blood Group measurement based on Image Processing. Blood group identification is required prior to blood transfusions in emergency conditions. It is done before a blood transfusion in an emergency or when checking a person's blood group for donation. Currently, lab personnel perform tests manually in the laboratory. This takes time and may result in human mistake when determining blood type. The goal of the study survey is to use image processing to reduce the amount of physical labor required to identify blood groups. The presence or absence of agglutination reaction of blood with antigen will be used to determine the blood group.
Martin Dodek, Eva Miklovičová, Marián Tárník, "Correlation Method for Identification of a Nonparametric Model of Type 1 Diabetes", IEEE Access, vol.10, pp.106369-106385, 2022.
O. Mujahid, I. Contreras, and J. Vehi, ‘‘Machine learning techniques for hypoglycemia prediction: Trends and challenges,’’ Sensors, vol. 21, no. 2, pp. 1–21, 2021, doi: 10.3390/s21020546.
E. I. Georga, V. C. Protopappas, D. Polyzos, and D. I. Fotiadis, ‘‘Online prediction of glucose concentration in type 1 diabetes using extreme learning machines,’’ in Proc.IEEE Eng. Med. Biol.(EMBC), Aug. 2020, pp. 3262–3265, doi: 10.1109/EMBC.2015.7319088.
G. P. Incremona, M. Messori, C. Toffanin, C. Cobelli, and L. Magni, ‘‘Model predictive control with integral action for artificial pancreas,’’ Control Eng. Pract., vol. 77, pp. 86–94, Aug. 2018, doi: 10.1016/j.conengprac.2018.05.006.
D. Rodbard, ‘‘Continuous glucose monitoring: A review of recent studies demonstrating improved glycemic outcomes,’’ Diabetes Technol. Therapeutics, vol. 19, no. S3, pp. S-25–S-37, Jun. 2017, doi: 10.1089/dia.2017.0035.
M. Miyashita, N. Ito, S. Ikeda, T. Murayama, K. Oguma, and J. Kimura ,―Development of urine Blood Grouping meter based on micro-planer amperometric biosensor and its clinical application for self-monitoring of urine Blood Grouping,‖ Biosensors Bioelectron., vol. 24, no. 5, pp. 1336–1340, 2009.
B Godber, KS Thompson, et. al., ―Direct quantification of analyte concentration by resonant acoustic profiling‖, Clinical Chemistry, vol 51, issue 10, pp. 1962 – 1972, Oct. 2005.
Kudo H., Sawada T., Kazawa E., Yoshida H., Iwasaki Y. and Mitsubayashi K., 2006. A flexible and wearable Blood Grouping sensor based on functional polymers with Soft-MEMS techniques. Biosens. Bioelectronics 22, 558-562
A. Boutayeb and S. Boutayeb, ―The burden of non-communicable diseases in developing countries,‖ Int. J. Equity Health, vol. 4, 2, January 2005.
N. J. van Haeringen, ―Clinical biochemistry of tears‖, Survey of Ophthalmology, vol. 26, pp. 84-96, September 1981.
J. T. Baca, C. R. Taormina, E. Feingold, D. N. Finegold, J. J. Grabowski, and A. Asher, ‖Mass spectral determination of fasting tear Blood Grouping concentration in nondiabetic volunteers,‖ Clin. Chem., vol. 53, pp. 1370-1372, July 2007.
E. Saeedi, S. Kim, H. Ho, and B. A. Parviz, ―Self-assembled crystalling semiconductor optoelectronics on glass and plastic,‖ J. Micromech. Microeng., vol.18, pp. 1-7, June 2008.
S. Lin, Y. Hwang, and Y. Tsai, ―Immobilization of glucooligosaccharide oxidase of Acremonium strictum for oligosaccharic acid production,― Biotechn. Tech., vol. 10, pp.63-168, 1996.
Mumin AM, Barrett JW, Dekaban GA, Zhang J. Dendritic cell internalization of foam-structured fluorescent mesoporous silica nanoparticles. J Colloid Interface Sci. 2011;353(1):156-62.
Nelson LA, McCann JC, Loepke AW, Wu J, Ben Dor B, Kurth CD. Development and validation of a multiwavelength spatial domain near-infrared oximeter to detect cerebral hypoxia-ischemia.J Biomed Opt. 2006;11(6):064022.
Sen DK, Sarin GS. Tear Blood Grouping levels in normal people and indiabetic patients. Br J Ophthalmol. 1980;64(9):693-5.
IEEE Standard for Safety Levels With Respect to Human Exposure to Radio Frequency Electromagnetic Fields, 3 kHz to 300 GHz, IEEE Std C95.1-2005 (Revision of IEEE Std C95.1-1991), 2006, pp. 1–238.
M. Ahmadi and G. Jullien, ―A wireless-implantable microsystem for continuous Blood Grouping monitoring,‖ IEEE Trans. Biomed. Circuits Syst., vol. 3, no. 3, pp. 169–180, Jun. 2009.
J. Pandey, Y.-T. Liao, A. Lingley, R. Mirjalili, B. Parviz, and B. Otis,―A fully integrated rf-powered contact lens with a single element display,‖IEEE Trans. Biomed. Circuits Syst., vol. 4, no. 6, pp. 454–461,Dec. 2010.
C. D. Geddes and J. R. Lakowicz, Blood Grouping Sensing. New York: Springer, 2006.
Jin, Z., Chen, R., Colon, L.A., 1997. Determination of Blood Grouping in Submicroliter Samples by CE-LIF Using Precolumn or On-Column Enzymatic Reactions. Anal. Chem. 69, 1326-1331.
O.S. Khalil, Non-invasive Blood Grouping measurement technologies: an update from 1999 to the dawn of the new millennium, Diabetes Technol. Ther. 6 (2004)660697.
A. Domschke, W.F. March, S. Kabilan, C. Lowe, Initial clinical testing of holographic non-invasive contact lens Blood Grouping sensor, Diabetes Technol.Ther.8 (2006) 89–93
Goldstein RJ, Chiang HD: Measurement of temperature and heat transfer. In Handbook of Heat Transfer Applications. Rohenow WM, Hartnett JP, Ganic EN, Eds.New York, McGraw-Hill, 1985, p. 12-1-12-94.
Gough DA: The composition and optical rotary dispersion of bovine aqueous humor. Diabetes Care 5:266-270, 1982
Diabetes Control and Complication Trial Research Group, ―The effect of intensive treatment of diabetes on the long-term complications in insulin dependent diabetes‖, New Ens. 3. Med. 329 97746, 1993.