Skip to main content

Advertisement

SpringerLink
Log in

IoT-inspired smart home based urine infection prediction

  • Original Research
  • Published:
Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Urine infection (UI) has been the most prevalent diseases among the global population. With large emphasize on smart healthcare in modern era, UI monitoring has been a major concern for medical industry. Motivated from these aspects, this paper proposes a novel Internet of Things-inspired framework in the form of a smart UI monitoring and prediction system for regular analysis of UI in home-centric environment. The overall framework has been structured in 5-layered model for determination of UI at early stages. These layers include urine perception layer, urine analysis layer, urine extraction layer, Urine prediction layer and visualization layer. These layers are designed to provide effective UI monitoring and analysis in real-time based on mathematical quantification of UI parameters in terms of infection degree and infection index measure for early prediction of UI. For prediction purposes, temporal-artificial neural network model is proposed. The visualization of the UI can be performed in time-sensitive manner over a handheld device for feasible analysis by the user. The validation of the proposed framework was done over real-time data of 12 patients acquired from nearby clinical laboratories. Results were compared with several state-of-the-art prediction approaches which show that the presented technique attain significant enhancement and high efficacy.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

References

  • Agana MA, Ofem OA, Ele BI (2019) A framework for a fuzzy smart home iot e-health support system. Future of information and communication conference. Springer, Berlin, pp 432–447

    Google Scholar 

  • Arif N, Qadir MI (2019) Relation between the presence or absence of chin dimple with urobilinogen in urine. Int J Adv Res Pharm Educ 1(1):1–3

    Google Scholar 

  • Bae JH, Lee HK (2018) User health information analysis with a urine and feces separable smart toilet system. IEEE Access 6:78751–78765

    Article  Google Scholar 

  • Bhatia M, Sood SK (2016) Temporal informative analysis in smart-icu monitoring: M-healthcare perspective. J Med Syst 40(8):190

    Article  Google Scholar 

  • Björck L, Christensson B, Herwald H, Linder A, Akesson P (2019) Diagnostic method for urinary tract infection. US Patent App. 16/138555

  • Boiarski A, Dosmann A (1997) Fiber optic diffuse light reflectance sensor utilized in the detection of occult blood. US Patent 5701181

  • Buyya R, Yeo CS, Venugopal S, Broberg J, Brandic I (2009) Cloud computing and emerging it platforms: vision, hype, and reality for delivering computing as the 5th utility. Future Gener Comput Syst 25(6):599–616

    Article  Google Scholar 

  • Catherwood PA, Steele D, Little M, Mccomb S, McLaughlin J (2018) A community-based iot personalized wireless healthcare solution trial. IEEE J Transl Eng Health Med 6:1–13

    Article  Google Scholar 

  • Chin J, Tisan A (2015) An IoT-based pervasive body hydration tracker (PHT). In: 2015 IEEE 13th international conference on industrial informatics (INDIN), IEEE, pp 437–441

  • Christobel YA, Sivaprakasam P (2013) A new classwise k nearest neighbor (CKNN) method for the classification of diabetes dataset. Int J Eng Adv Technol 2(3):396–400

    Google Scholar 

  • Çiçek Ç, Yılmaz F, Özgür E, Yavuz H, Denizli A (2016) Molecularly imprinted quartz crystal microbalance sensor (QCM) for bilirubin detection. Chemosensors 4(4):21

    Article  Google Scholar 

  • Dangare CS, Apte SS (2012) Improved study of heart disease prediction system using data mining classification techniques. Int J Comput Appl 47(10):44–48

    Google Scholar 

  • Darwish A, Hassanien AE, Elhoseny M, Sangaiah AK, Muhammad K (2019) The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems. J Ambient Intell Humaniz Comput 10(10):4151–4166

    Article  Google Scholar 

  • Elavarasi MK, Suganthi MV, Jayachitra MJ (2018) Developing smart toilets using iot. Int J Pure Appl Math 119(14):611–618

    Google Scholar 

  • Eldar R, Ring H, Tshuwa M, Dynia A, Ronen R (2001) Quality of care for urinary incontinence in a rehabilitation setting for patients with stroke. Simultaneous monitoring of process and outcome. Int J Qual Health Care 13(1):57–61

    Article  Google Scholar 

  • Erenas MM, Ortiz-Gómez I, de Orbe-Payá I, Hernández-Alonso D, Ballester P, Blondeau P, Andrade FJ, Salinas-Castillo A, Capitan-Vallvey LF (2019) Ionophore-based optical sensor for urine creatinine determination. ACS Sens 20:20

    Google Scholar 

  • Esposito C, De Santis A, Tortora G, Chang H, Choo KKR (2018) Blockchain: a panacea for healthcare cloud-based data security and privacy? IEEE Cloud Comput 5(1):31–37

    Article  Google Scholar 

  • Eswari T, Sampath P, Lavanya S et al (2015) Predictive methodology for diabetic data analysis in big data. Proced Comput Sci 50:203–208

    Article  Google Scholar 

  • Flores-Mireles AL, Walker JN, Caparon M, Hultgren SJ (2015) Urinary tract infections: epidemiology, mechanisms of infection and treatment options. Nat Rev Microbiol 13(5):269–284

    Article  Google Scholar 

  • Guay R, Moffatt PR (1983) Electronic hydrometer and method of determining the density of a liquid. US Patent 4,400,978

  • Guo T, Liu F, Liang X, Qiu X, Huang Y, Xie C, Xu P, Mao W, Guan BO, Albert J (2016) Highly sensitive detection of urinary protein variations using tilted fiber grating sensors with plasmonic nanocoatings. Biosens Bioelectron 78:221–228

    Article  Google Scholar 

  • Harimoorthy K, Thangavelu M (2020) Multi-disease prediction model using improved svm-radial bias technique in healthcare monitoring system. J Ambient Intell Human Comput 20:1–9

    Google Scholar 

  • Hossain MS, Xu C, Li Y, Bilbao J, El Saddik A (2018) Advances in next-generation networking technologies for smart healthcare. IEEE Commun Mag 56(4):14–15

    Article  Google Scholar 

  • Kalb IM, Shaw RH, Ram MJ (1998) Urinary diagnostic catheter. US Patent 5,704,353

  • Karthigaiveni M, Indrani B (2019) An efficient two-factor authentication scheme with key agreement for IoT based e-health care application using smart card. J Ambient Intell Human Comput 20:1–12

    Google Scholar 

  • Kaur PD, Sharma P (2019) Ic-smart: Iotcloud enabled seamless monitoring for alzheimer diagnosis and rehabilitation system. J Ambient Intell Human Comput 20:1–17

    Google Scholar 

  • Kharya S (2012) Using data mining techniques for diagnosis and prognosis of cancer disease. arXiv:12051923 (arXiv preprint)

  • Ling ZJ, Tran QT, Fan J, Koh GC, Nguyen T, Tan CS, Yip JW, Zhang M (2014) Gemini: an integrative healthcare analytics system. Proc VLDB Endow 7(13):1766–1771

    Article  Google Scholar 

  • Manogaran G, Varatharajan R, Lopez D, Kumar PM, Sundarasekar R, Thota C (2018) A new architecture of internet of things and big data ecosystem for secured smart healthcare monitoring and alerting system. Future Gener Comput Syst 82:375–387

    Article  Google Scholar 

  • Mayer P, Güldenpfennig F, Panek P (2019) Towards smart adaptive care toilets. Stud Health Technol Inform 260:9–16

    Google Scholar 

  • Mohanty MD, Mohanty MN (2019) A cognitive approach for design of smart toilet in healthcare units. Cognitive informatics and soft computing. Springer, Berlin, pp 771–780

    Chapter  Google Scholar 

  • Mohanty MD, Pattnaik D, Parida M, Mohanty S, Mohanty MN (2019) Design of intelligent pid controller for smart toilet of ccu/icu patients in healthcare systems. International conference on intelligent computing and applications. Springer, Berlin, pp 97–107

    Chapter  Google Scholar 

  • Penders J, Fiers T, Delanghe JR (2002) Quantitative evaluation of urinalysis test strips. Clin Chem 48(12):2236–2241

    Article  Google Scholar 

  • Rantz MJ, Skubic M, Koopman RJ, Phillips L, Alexander GL, Miller SJ, Guevara RD (2011) Using sensor networks to detect urinary tract infections in older adults. In: 2011 IEEE 13th international conference on e-health networking. Applications and services, IEEE, pp 142–149

  • Raskin RE, Murray KA, Levy JK (2002) Comparison of home monitoring methods for feline urine ph measurement. Vet Clin Pathol 31(2):51–55

    Article  Google Scholar 

  • Richards D, Toop L, Chambers S, Fletcher L (2005) Response to antibiotics of women with symptoms of urinary tract infection but negative dipstick urine test results: double blind randomised controlled trial. BMJ 331(7509):143

    Article  Google Scholar 

  • Robinson HL, Barrett HL, Foxcroft K, Callaway LK, Dekker Nitert M (2018) Prevalence of maternal urinary ketones in pregnancy in overweight and obese women. Obstetr Med 11(2):79–82

    Article  Google Scholar 

  • Seo W, Yu W, Tan T, Ziaie B, Jung B (2017) Diaper-embedded urinary tract infection monitoring sensor module powered by urine-activated batteries. IEEE Trans Biomed Circuits Syst 11(3):681–691

    Article  Google Scholar 

  • Shafqat S, Kishwer S, Rasool RU, Qadir J, Amjad T, Ahmad HF (2018) Big data analytics enhanced healthcare systems: a review. J Supercomput 20:1–46

    Google Scholar 

  • Shahid N, Rappon T, Berta W (2019) Applications of artificial neural networks in health care organizational decision-making: a scoping review. PLoS One 14(2):e0212356

    Article  Google Scholar 

  • Ultsch A (2003) U*-matrix: a tool to visualize clusters in high dimensional data

  • Ultsch A (1993) Self-organizing neural networks for visualisation and classification. Information and classification. Springer, Berlin, pp 307–313

    Chapter  Google Scholar 

  • Wang Cs, Boyd R, Mitchell R, Wright WD, McCracken C, Escoffery C, Patzer RE, Greenbaum LA (2019) Development of a novel mobile application to detect urine protein for nephrotic syndrome disease monitoring. BMC Med Inform Decis Mak 19(1):105

    Article  Google Scholar 

  • Yin W, Qin W, Gao Y (2018) Urine glucose levels are disordered before blood glucose level increase was observed in zucker diabetic fatty rats. Sci China Life Sci 61(7):844–848

    Article  Google Scholar 

  • Zanetti M, Mizumoto T, Faes L, Fornaser A, De Cecco M, Maule L, Valente M, Nollo G (2019) Multilevel assessment of mental stress via network physiology paradigm using consumer wearable devices. J Ambient Intell Human Comput 20:1–10

    Google Scholar 

  • Zimmerle CT, Pugia MJ (2018) Device and method for detection of humidity-compromised urine test strips. US Patent App. 15/904,823

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India

    Munish Bhatia

  2. Amritsar College of Engineering and Technology, Amritsar, Punjab, India

    Simranpreet Kaur

  3. Department of Computer Science and Informatics, Central University, Dharamshala, H.P., India

    Sandeep K. Sood

Authors
  1. Munish Bhatia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Simranpreet Kaur
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sandeep K. Sood
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Munish Bhatia.

Ethics declarations

Conflict of interest

Authors have no conflict of interest with anyone.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Bhatia, M., Kaur, S. & Sood, S.K. IoT-inspired smart home based urine infection prediction. J Ambient Intell Human Comput 14, 5249–5263 (2023). https://doi.org/10.1007/s12652-020-01952-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12652-020-01952-w

Keywords

Search

Navigation