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A novel fog-computing-assisted architecture of E-healthcare system for pregnant women

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Abstract

Recently, there is a tremendous rise and adoption of smart wearable devices in smart healthcare applications. Moreover, the advancement in sensors and communication technology empowers to detect and analyse physiological data of an individual from the wearable device. At present, the smart wearable device based on internet of things is assisting the pregnancy woman to continuously monitor their health status for avoiding the severity. The physiological data analysis of wearable device is processed with the assistance of fog computing due to limited computational and energy capability in the wearable device. Additionally, fog computing overcomes the excess latency that is created by cloud computing during physiological data analysis. In this article, a smart health monitoring IoT and fog-assisted framework are proposed for obtaining and processing the temperature, blood pressure, ECG, and pulse oximeter parameters of the pregnant woman. Based on real time series data, the rule-based algorithm logged in the wearable device with fog computing to analyse the critical health conditions of pregnant women. The proposed wearable device is validated and tested on 80 pregnant women in real time, and wearable device is delivering the 98.75% accuracy in providing health recommendations.

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Abbreviations

IoT:

Internet of things

E-Healthcare:

Electronic healthcare

ECG:

Electrocardiogram

FAAL:

Fog ambient assisted living

BAN:

Body area network

PSoV:

Probabilistic state of vulnerability

EMG:

Electromyography

RF:

Radio frequency

SSL:

Secure socket layer

DoV:

Degree of vulnerability

BBN:

Bayesian belief network

ID:

Identification

ANN:

Artificial neural network

WiFi:

Wireless fidelity

GSM:

Global system for mobile communications

PPM:

Parts per million

MAE:

Mean absolute error

RMSE:

Root-mean-squared error

SBP:

Systolic blood pressure

DBP:

Diastolic blood pressure

FTDI:

Future technology devices international

MCU:

Microcontroller unit

PCB:

Printed circuit board

MQTT:

MQ telemetry transport

References

  1. Lewis G (2008) Maternal mortality in the developing world: Why do mothers really die? Obstet Med. https://doi.org/10.1258/om.2008.080019

    Article  Google Scholar 

  2. Thairu L (2012) Medical devices for pregnancy and childbirth in the developing world. Health Technol (Berl). https://doi.org/10.1007/s12553-012-0033-4

    Article  Google Scholar 

  3. Barua M, Liang X, Lu R, Shen X (2011) PEACE: an efficient and secure patient-centric access control scheme for eHealth care system. In: 2011 IEEE Conference on Computer Communications Workshops, INFOCOM WKSHPS 2011

  4. Ren Y, Werner R, Pazzi N, Boukerche A (2010) Monitoring patients via a secure and mobile healthcare system. IEEE Wirel Commun. https://doi.org/10.1109/MWC.2010.5416351

    Article  Google Scholar 

  5. Thirugnanam T, Ghalib MR (2020) A new healthcare architecture using IoV technology for continuous health monitoring system. Health Technol (Berl). https://doi.org/10.1007/s12553-019-00306-7

    Article  Google Scholar 

  6. El-Latif AAA, Hossain MS, Wang N (2019) Score level multibiometrics fusion approach for healthcare. Cluster Comput 22:2425–2436. https://doi.org/10.1007/s10586-017-1287-4

    Article  Google Scholar 

  7. Oti O, Azimi I, Anzanpour A, et al (2019) Iot-based healthcare system for real-time maternal stress monitoring. In: Proceedings–2018 IEEE/ACM International Conference on Connected Health: Applications, Systems and Engineering Technologies, CHASE 2018

  8. Kadarina TM, Priambodo R (2018) Preliminary design of internet of things (IoT) application for supporting mother and child health program in Indonesia. In: 2017 International Conference on Broadband Communication, Wireless Sensors and Powering, BCWSP 2017

  9. Azimi I, Pahikkala T, Rahmani AM et al (2019) Missing data resilient decision-making for healthcare IoT through personalization: a case study on maternal health. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2019.02.015

    Article  Google Scholar 

  10. Singh B, Acharjya DP (2020) Computational intelligence techniques for efficient delivery of healthcare. Health Technol (Berl). https://doi.org/10.1007/s12553-018-00280-6

    Article  Google Scholar 

  11. Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of things (IoT): a vision, architectural elements, and future directions. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2013.01.010

    Article  Google Scholar 

  12. Li S, Da XuL, Wang X (2013) Compressed sensing signal and data acquisition in wireless sensor networks and internet of things. IEEE Trans Ind Informat. https://doi.org/10.1109/TII.2012.2189222

    Article  Google Scholar 

  13. Tekeste Habte T, Saleh H, Mohammad B, Ismail M (2019) Introduction to ultra-low power ECG processor. In: Saleh H, Mohammad B, Ismail M (eds) Temesghen Tekeste Habte Ultra Low Power ECG Processing System for IoT Devices. Springer, Cham

    Google Scholar 

  14. Gul N, Khan MS, Kim SM et al (2020) Particle swarm optimization in the presence of malicious users in cognitive IoT networks with data. Sci Program. https://doi.org/10.1155/2020/8844083

    Article  Google Scholar 

  15. Tuli S, Basumatary N, Gill SS et al (2020) HealthFog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2019.10.043

    Article  Google Scholar 

  16. Nguyen Gia T, Jiang M, Sarker VK, et al (2017) Low-cost fog-assisted health-care IoT system with energy-efficient sensor nodes. In: 2017 13th International Wireless Communications and Mobile Computing Conference, IWCMC 2017

  17. Beri R, Dubey MK, Gehlot A, Singh R (2020) A Study of E-Healthcare System for Pregnant Women. In: 2nd international conference on futuristic trends in networks and communication technologies at: C-DAC mohali

  18. Mutlag AA, Abd Ghani MK, Arunkumar N et al (2019) Enabling technologies for fog computing in healthcare IoT systems. Futur Gener Comput Syst. https://doi.org/10.1016/j.future.2018.07.049

    Article  Google Scholar 

  19. El-Latif AAA, Abd-El-Atty B, Hossain MS et al (2018) Secure quantum steganography protocol for fog cloud internet of things. IEEE Access. https://doi.org/10.1109/ACCESS.2018.2799879

    Article  Google Scholar 

  20. Vora J, Tanwar S, Tyagi S, et al (2017) FAAL: fog computing-based patient monitoring system for ambient assisted living. In: 2017 IEEE 19th International Conference on E-Health Networking, Applications and Services, Healthcom 2017

  21. El-Latif AAA, Abd-El-Atty B, Mazurczyk W et al (2020) Secure data encryption based on quantum walks for 5G internet of things scenario. IEEE Trans Netw Serv Manag. https://doi.org/10.1109/TNSM.2020.2969863

    Article  Google Scholar 

  22. Wang SL, Chen YL, Kuo AMH et al (2016) Design and evaluation of a cloud-based mobile health information recommendation system on wireless sensor networks. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2015.07.017

    Article  Google Scholar 

  23. Sadek I, Demarasse A, Mokhtari M (2020) Internet of things for sleep tracking: wearables versus nonwearables. Health Technol. https://doi.org/10.1007/s12553-019-00318-3

    Article  Google Scholar 

  24. Liu H, Li J, Xia W et al (2019) Blood pressure changes during pregnancy in relation to urinary paraben, triclosan and benzophenone concentrations: a repeated measures study. Environ Int. https://doi.org/10.1016/j.envint.2018.11.003

    Article  Google Scholar 

  25. Dali BB (2014) Maternal mortality in pregnancy with heart disease. J Inst Med 37:15–18

    Google Scholar 

  26. Sakr S, Elgammal A (2016) Towards a comprehensive data analytics framework for smart healthcare services. Big Data Res. https://doi.org/10.1016/j.bdr.2016.05.002

    Article  Google Scholar 

  27. Marin I, Goga N, Doncescu A (2019) Sentiment analysis electronic healthcare system based on heart rate monitoring smart bracelet. In: Proceedings - IEEE 11th International Conference on Service-Oriented Computing and Applications, SOCA 2018

  28. Moreira MWL, Rodrigues JJPC, Furtado V et al (2019) Averaged one-dependence estimators on edge devices for smart pregnancy data analysis. Comput Electr Eng. https://doi.org/10.1016/j.compeleceng.2018.07.041

    Article  Google Scholar 

  29. Quasim MT, Khan MA, Abdullah M, et al (2019) Internet of things for smart healthcare: a hardware perspective. In: 2019 1st International Conference of Intelligent Computing and Engineering: Toward Intelligent Solutions for Developing and Empowering our Societies, ICOICE 2019

  30. Mugoye K, Okoyo H, McOyowo S (2019) Smart-bot technology: conversational agents role in maternal healthcare support. In: 2019 IST-Africa Week Conference, IST-Africa 2019

  31. Moreira MWL, Rodrigues JJPC, Kumar N et al (2019) Postpartum depression prediction through pregnancy data analysis for emotion-aware smart systems. Inf Fusion 47:23–31. https://doi.org/10.1016/J.INFFUS.2018.07.001

    Article  Google Scholar 

  32. Bin Queyam A, Kumar Meena R, Kumar Pahuja S, Singh D (2018) An IoT based multi-parameter data acquisition system for efficient bio-telemonitoring of pregnant women at home. Proceedings 8th Internatinal Conference Conflu 2018 Cloud Comput Data Sci Eng Conflu 2018 618–624. https://doi.org/10.1109/CONFLUENCE.2018.8442686

  33. Omesh Reddy L, Venkata Manikanta DPA (2020) Healthcare Management for Pregnant Lady Using Iot. SSRN Libr

  34. Oti O, Azimi I, Anzanpour A, et al (2019) Iot-based healthcare system for real-Time maternal stress monitoring. Proceedings 2018 IEEE/ACM International Conferences Connect Heal Applied System Engineering Technology CHASE 2018 57–62. https://doi.org/10.1145/3278576.3278596

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Acknowledgements

The authors would like to acknowledge the support received from Taif University Researchers Supporting Project Number (TURSP-2020/147), Taif university, Taif, Saudi Arabia.

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Authors and Affiliations

  1. School of Computer Applications, Lovely Professional University, Phagwara, Kapurthala, Punjab, 144411, India

    Rydhm Beri & Mithilesh K. Dubey

  2. School of Electronics and Electrical Engineering, Lovely Professional University, Phagwara, Kapurthala, Punjab, 14411, India

    Anita Gehlot & Rajesh Singh

  3. Department of Computer Engineering, College of Computers and Information Technology, Taif University, P.O. Box 11099, Taif, 21944, Saudi Arabia

    Mohammed Abd-Elnaby

  4. School of Computer Science and Engineering, Lovely Professional University, Phagwara, Kapurthala, Punjab, 144411, India

    Aman Singh

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  1. Rydhm Beri
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  4. Rajesh Singh
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  5. Mohammed Abd-Elnaby
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Correspondence to Aman Singh.

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Beri, R., Dubey, M.K., Gehlot, A. et al. A novel fog-computing-assisted architecture of E-healthcare system for pregnant women. J Supercomput 78, 7591–7615 (2022). https://doi.org/10.1007/s11227-021-04176-7

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