WATER INTAKE APPLET BASED ON HUMAN EXCREMENT
DOI:
https://doi.org/10.34288/jri.v3i2.56Keywords:
Water Intake, Dehydration, Color, Human ExcrementAbstract
To function properly, the human body requires adequate hydration as 70% of the human body was being built up by water. It acts as a solvent for lots of biochemical reactions to keep our physiological function work optimally throughout the day. Dehydration could cause a disturbance in both the gastrointestinal and kidney systems of human beings, and it could be noticed in the color of both urine and feces. The objective of this paper is to present an idea on how to make a water intake app based on the color indicator of human excrement surveillance. It might be a solution to intensify people's ability to become self-conscious when drinking less water by surveying the excreted substance. The deployed method is to measure one indicator which is between urine and feces detector on how much water should the user drink by observing the color of both their urine and feces. However, both excrement indicators are needed to detect users' drinking amount. If one of these indicators shows a bad result, it could lead to water intoxication or hydration. The application has been successfully created using Python to give feedback for the user's water intake based on the condition of their excreted substance. The Water Intake application has successfully shown a clear indicator for dehydration. It could be inferred that this water intake software could detect the dehydration phenomenon with human excrement as the main indicator.
Downloads
References
Archie, M. (2019). Creating a Cliché Library for Social Applications (Massachusetts Institute of Technology). Massachusetts Institute of Technology, Cambridge. Retrieved from https://dspace.mit.edu/handle/1721.1/123001
Aversa, R., Petrescu, V., Apicella, A., & Petrescu, I. T. (2016). The basic elements of life’s. American Journal of Engineering and Applied Sciences, 9(4), 1189–1197. https://doi.org/10.3844/ajeassp.2016.1189.1197
Bernard, S., & Parikesit, A. A. (2020). Artificial Intelligence in Colonoscopy: Improving Medical Diagnostic of Colorectal Cancer. Frontiers in Health Informatics, 9(1), 33. https://doi.org/10.30699/fhi.v9i1.209
Bhat, N., Wijaya, E. B., & Parikesit, A. A. (2019). Use of the “DNAChecker” Algorithm for Improving Bioinformatics Research. Makara Journal of Technology, 23(2), 72. https://doi.org/10.7454/mst.v23i2.3488
Bičanić, I., Hladnik, A., Džaja, D., & Petanjek, Z. (2019). The anatomy of orofacial innervation. Acta Clinica Croatica, 58(Supplement 1), 35–42. https://doi.org/10.20471/acc.2019.58.s1.05
Broom, A. (2005). Virtually healthy: The impact of internet use on disease experience and the doctor-patient relationship. Qualitative Health Research, 15(3), 325–345. https://doi.org/10.1177/1049732304272916
Carmichael, A. (2011). Initial treatment of dehydration for severe acute malnutrition.
Cheuvront, S. N., Muñoz, C. X., & Kenefick, R. W. (2016). The void in using urine concentration to assess population fluid intake adequacy or hydration status 1,2. The American Journal of Clinical Nutrition, 104(3), 553–556. https://doi.org/10.3945/ajcn.115.129858
Everyone. (2020). Water Reminder - Remind Drink Water. Hanoi: Smart Apps OGS Studio. Retrieved from https://play.google.com/store/apps/details?id=com.remind.drink.water.hourly&hl=en_US&gl=US
Fogel, A. L., & Kvedar, J. C. (2018). Artificial intelligence powers digital medicine. Npj Digital Medicine, 1(5), 1–4. https://doi.org/10.1038/s41746-017-0012-2
Ford, B. A., & McElvania, E. (2020). Machine learning takes laboratory automation to the next level. Journal of Clinical Microbiology, 58(4), 1683–1702. https://doi.org/10.1128/JCM.00012-20
Fujimoto, T., Hashimoto, T., Sakaki, H., Higashi, Y., Tamura, T., & Tsuji, T. (1998). Automated handling system for excretion. Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 4, 1973–1976. Hong Kong: IEEE. https://doi.org/10.1109/iembs.1998.746989
Funn Media. (2020). WaterMinder - Water Tracker and Drink Reminder App. Cicero Ave: Funn Media. Retrieved from https://play.google.com/store/apps/details?id=com.funnmedia.waterminder
Ghasemi, E., Khorvash, M., Ghorbani, G. R., & Elmamouz, F. (2014). Effects of straw treatment and nitrogen supplementation on digestibility, intake and physiological responses of water intake as well as urine and faecal characteristics. Journal of Animal Physiology and Animal Nutrition, 98(1), 100–106. https://doi.org/10.1111/jpn.12052
Grandjean, A. (2014). Water Requirements, Impinging Factors, and Recommended Intakes. Geneva.
Guo, Q., Wang, B., Cao, S., Jia, C., Zhao, L., Zhang, Q., … Duan, X. (2020). Patterns and sociodemographic determinants of water intake by children in China: results from the first national population-based survey. European Journal of Nutrition, 59(2), 529–538. https://doi.org/10.1007/s00394-019-01921-w
Ivan, J., Nurdiansyah, R., & Parikesit, A. A. (2019). Mathematical Problem Solving: One Way to Prevent Dementia. Frontiers in Health Informatics, 8(1), 14. https://doi.org/10.30699/fhi.v8i1.179
Jéquier, E., & Constant, F. (2010). Water as an essential nutrient: the physiological basis of hydration. European Journal of Clinical Nutrition, 64, 115–123. https://doi.org/10.1038/ejcn.2009.111
Kenefick, R. W., & Sawka, M. N. (2007). Hydration at the Work Site. Journal of the American College of Nutrition, 26, 597S-603S. https://doi.org/10.1080/07315724.2007.10719665
Kobrinskii, B. A. (2020). Fuzzy and Reflection in the Construction of a Medical Expert System. Journal of Software Engineering and Applications, 13(02), 15–23. https://doi.org/10.4236/jsea.2020.132002
Liao, J. C., & Churchill, B. M. (2001). Pediatric urine testing. Pediatric Clinics of North America, 48(6), 1425–1440. https://doi.org/10.1016/S0031-3955(05)70384-9
Liska, D., Mah, E., Brisbois, T., Barrios, P. L., Baker, L. B., & Spriet, L. L. (2019). Narrative Review of Hydration and Selected Health Outcomes in the General Population. Nutrients, 11(1), 70–99. https://doi.org/10.3390/nu11010070
Malisova, O., Bountziouka, V., Panagiotakos, D. Β., Zampelas, A., & Kapsokefalou, M. (2013). Evaluation of seasonality on total water intake, water loss and water balance in the general population in Greece. Journal of Human Nutrition and Dietetics, 26(SUPPL.1), 90–96. https://doi.org/10.1111/jhn.12077
Nguyen, V. P. (2014). An open source program to generate zero-thickness cohesive interface elements. Advances in Engineering Software, 74(August), 27–39. https://doi.org/10.1016/j.advengsoft.2014.04.002
Park, S. min, Won, D. D., Lee, B. J., Escobedo, D., Esteva, A., Aalipour, A., … Gambhir, S. S. (2020). A mountable toilet system for personalized health monitoring via the analysis of excreta. Nature Biomedical Engineering, 4(6), 624–635. https://doi.org/10.1038/s41551-020-0534-9
Perrier, E. T., Armstrong, L. E., Bottin, J. H., Clark, W. F., Dolci, A., Guelinckx, I., … Péronnet, F. (2020, July). Hydration for health hypothesis: a narrative review of supporting evidence. European Journal of Nutrition, pp. 1–14. Springer. https://doi.org/10.1007/s00394-020-02296-z
Popkin, B. M., D’Anci, K. E., & Rosenberg, I. H. (2010). Water, hydration, and health. Nutrition Reviews, Vol. 68, pp. 439–458. Blackwell Publishing Inc. https://doi.org/10.1111/j.1753-4887.2010.00304.x
Riebl, S. K., & Davy, B. M. (2013). The Hydration Equation. ACSM’S Health & Fitness Journal, 17(6), 21–28. https://doi.org/10.1249/FIT.0b013e3182a9570f
Rose, C., Parker, A., Jefferson, B., Cartmell, E., & Rose, © C. (2015). The Characterization of Feces and Urine: A Review of the Literature to Inform Advanced Treatment Technology. Critical Reviews in Environmental Science and Technology, 45, 1827–1879. https://doi.org/10.1080/10643389.2014.1000761
Sawka, M. N., Cheuvront, S. N., & Carter, R. (2005). Human Water Needs. Nutrition Reviews, 63(suppl_1), S30–S39. https://doi.org/10.1111/j.1753-4887.2005.tb00152.x
Shu, Z., Liu, G., Xie, Q., & Ren, Z. (2017). A method of urine detection based on front vision and image recognition. Proceedings - 2016 9th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics, CISP-BMEI 2016, 402–405. Datong: Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/CISP-BMEI.2016.7852744
Stachenfeld, N. S., Leone, C. A., Mitchell, E. S., Freese, E., & Harkness, L. (2018). Water intake reverses dehydration associated impaired executive function in healthy young women. Physiology and Behavior, 185(1), 103–111. https://doi.org/10.1016/j.physbeh.2017.12.028
Stookey, J. D. (2019). Analysis of 2009-2012 Nutrition Health and Examination Survey (NHANES) Data to Estimate the Median Water Intake Associated with Meeting Hydration Criteria for Individuals Aged 12-80 Years in the US Population. https://doi.org/10.3390/nu11030657
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2021 Nadine Swastika, Winda Hasuki, Sava Savero, Putri Gabriella Satya, Arli Aditya Parikesit
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The Jurnal Riset Informatika has legal rules for accessing digital electronic articles uunder a Creative Commons Attribution-NonCommercial 4.0 International License . Articles published in Jurnal Riset Informatika, provide Open Access, for the purpose of scientific development, research, and libraries.
