Skip to main content
SpringerLink
Log in

Integrating M-Health with IoMT to Counter COVID-19

  • Chapter
  • First Online:
Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis

Part of the book series: Studies in Computational Intelligence ((SCI,volume 923))

Abstract

 With the alarming escalation of COVID infection across the globe, there seems a dire need of upgrading the conventional medical practices and technologies in order to battle the pandemic. There are many technological advancements in the direction of integrating emergent technologies with the current medical practices for efficient treatment of COVID crisis and other therapeutic strategies. The proposed intent is aimed to explain such technological advancements like IoMT (Internet of Medical Things) and mHealth and their combined utilization in combating the COVID-19 pandemic. This chapter highlights the works done in integrating Medical Science with emergent technologies such as Robotics, Cognitive Radio System, Wearable and Ingestible Sensory devices, Mobile phones and their significance in providence of medical facilities at doorstep, and 5G technologies in Medical applications. All these elucidations are followed by highlighting their utilization in combating the COVID pandemic through various approaches. This chapter also discusses their current limitations in practical medical world and future aspects of advancements in the medical science.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Ashton, K. (2009). That ‘internet of things’ thing. RFID Journal, 22(7), 97–114.

    Google Scholar 

  2. Goetz, T. (2011). Harnessing the power of feedback loops. Wired magazine, 19(07).

    Google Scholar 

  3. Alam, M., Shakil, K. A., & Khan, S. (2020). Internet of Things (IoT).

    Google Scholar 

  4. International Cooperation Unites IEEE and CCSA For New “Internet of Things” Workshop— IEEE SA Beyond Standards. https://beyondstandards.ieee.org/iot/iotworkshop/.

  5. Security and IoT in IEEE Standards, IEEE Standards University.

    Google Scholar 

  6. Arbat, H., Choudhary, S., & Bala, K. (2016). IOT smart health band. Imperial Journal of Interdisciplinary Research, 2(5).

    Google Scholar 

  7. Joyia, G. J., Liaqat, R. M., Farooq, A., & Rehman, S. (2017). Internet of Medical Things (IOMT): Applications, benefits and future challenges in healthcare domain. J Commun, 12(4), 240–247.

    Google Scholar 

  8. Yang, T., Gentile, M., Shen, C. F., & Cheng, C. M. (2020). Combining point-of-care diagnostics and internet of medical things (IoMT) to combat the COVID-19 pandemic.

    Google Scholar 

  9. Clearprobe Ultrasound—Portable USB Device—Telemedicine—Global Med: https://www.globalmed.com/solutions/connected-devices/other-devices/clearprobe/.

  10. Toma, C., Alexandru, A., Popa, M., & Zamfiroiu, A. (2019). IoT solution for smart cities’ pollution monitoring and the security challenges. Sensors, 19(15), 3401.

    Article  Google Scholar 

  11. Marques, G., & Pitarma, R. (2019). A cost-effective air quality supervision solution for enhanced living environments through the internet of things. Electronics, 8(2), 170.

    Article  Google Scholar 

  12. Nallakaruppan, M. K., & Kumaran, U. S. (2019). IoT based machine learning techniques for climate predictive analysis.

    Google Scholar 

  13. Zhai, A. F., Cheng, B. M., Zhang, C. L., Ding, D. T., & Liu, E. Y. (2017). Optimization of agricultural production control based on data processing technology of agricultural internet of things. Italian Journal of Pure and Applied Mathematics, 243.

    Google Scholar 

  14. Zou, Y., & Quan, L. (2017). A new service-oriented grid-based method for AIoT application and implementation. Modern Physics Letters B, 31(19–21), 1740064.

    Article  Google Scholar 

  15. Alahmadi, A., Alwajeeh, T., Mohanan, V., & Budiarto, R. (2018). Wireless sensor network with always best connection for internet of farming. In Powering the internet of things with 5G networks (pp. 176–201). IGI Global.

    Google Scholar 

  16. Villa-Henriksen, A., Edwards, G. T., Pesonen, L. A., Green, O., & Sørensen, C. A. G. (2020). Internet of things in arable farming: Implementation, applications, challenges and potential. Biosystems Engineering, 191, 60–84.

    Article  Google Scholar 

  17. Killeen, P., Ding, B., Kiringa, I., & Yeap, T. (2019). IoT-based predictive maintenance for fleet management. Procedia Computer Science, 151, 607–613.

    Article  Google Scholar 

  18. Hasan, N., Chamoli, A., & Alam, M. (2020). Privacy challenges and their solutions in IoT. In Internet of things (IoT) (pp. 219–231). Springer, Cham.

    Google Scholar 

  19. Vishnu, S., Ramson, S. J., & Jegan, R. (2020, March). Internet of medical things (IoMT)-An overview. In 2020 5th International Conference on Devices, Circuits and Systems (ICDCS) (pp. 101–104). IEEE.

    Google Scholar 

  20. Ramson, S. R., & Moni, D. J. (2016). A case study on different wireless networking technologies for remote health care. Intelligent Decision Technologies, 10(4), 353–364.

    Article  Google Scholar 

  21. John, J. T., & Ramson, S. J. (2013). Energy-aware duty cycle scheduling for efficient data collection in wireless sensor networks. IJARCET, 2.

    Google Scholar 

  22. Raza, K. (2020). Artificial intelligence against COVID-19: A meta-analysis of current research. In Big Data Analytics and Artificial Intelligence Against COVID-19: Innovation Vision and Approach. Studies in Big Data, 78, 2020. Berlin: Springer (In Press).

    Google Scholar 

  23. Vishnu, S., Ramson, S. J., Raju, K. L., & Anagnostopoulos, T. (2019). Simple-link sensor network-based remote monitoring of multiple patients. In Intelligent data analysis for biomedical applications (pp. 237–252). Academic Press.

    Google Scholar 

  24. Vergara, P. M., De La Cal, E., Villar, J. R., González, V. M., & Sedano, J. (2017). An IoT platform for epilepsy monitoring and supervising. Journal of Sensors.

    Google Scholar 

  25. Yuce, M. R., & Dissanayake, T. (2013). Easy-to-swallow antenna and propagation. IEEE Microwave Magazine, 14(4), 74–82.

    Article  Google Scholar 

  26. World Health Organization (WHO), The world health report. (2016). Geneva. Switzerland, 2016, 8–9.

    Google Scholar 

  27. Annavarapu, A., Borra, S., & Kora, P. (2018). ECG signal dimensionality reduction-based atrial fibrillation detection. In Classification in bioApps (pp. 383–406). Springer, Cham.

    Google Scholar 

  28. Kora, P., Annavarapu, A., & Borra, S. (2018). ECG based myocardial infarction detection using different classification techniques. In Classification in bioApps (pp. 57–77). Springer, Cham.

    Google Scholar 

  29. Dey, N., Ashour, A. S., Chakraborty, S., Samanta, S., Sifaki-Pistolla, D., Ashour, A. S., et al. (2016). Healthy and unhealthy rat hippocampus cells classification: a neural based automated system for Alzheimer disease classification. Journal of Advanced Microscopy Research, 11(1), 1–10.

    Article  Google Scholar 

  30. Dey, N., Ashour, A. S., & Borra, S. (Eds.). (2017). Classification in BioApps: automation of decision making (Vol. 26). Springer.

    Google Scholar 

  31. Thanki, R., Borra, S., Dey, N., & Ashour, A. S. (2018). Medical imaging and its objective quality assessment: an introduction. In Classification in bioApps (pp. 3–32). Springer, Cham.

    Google Scholar 

  32. Guntur, S. R., Gorrepati, R. R., & Dirisala, V. R. (2019). Robotics in healthcare: an internet of medical robotic things (IoMRT) perspective. In Machine learning in bio-signal analysis and diagnostic imaging (pp. 293–318). Academic Press.

    Google Scholar 

  33. ITU, I. (2009). Definitions of software defined radio (SDR) and cognitive radio system (CRS).

    Google Scholar 

  34. Filin, S., Harada, H., Murakami, H., & Ishizu, K. (2011). International standardization of cognitive radio systems. IEEE Communications Magazine, 49(3), 82–89.

    Article  Google Scholar 

  35. Swayamsiddha, S., & Mohanty, C. (2020). Application of cognitive internet of medical things for COVID-19 pandemic. Diabetes and Metabolic Syndrome: Clinical Research and Reviews.

    Google Scholar 

  36. Bing, B. (Ed.). (2008). Emerging technologies in wireless LANs: Theory, design, and deployment, Cambridge University Press.

    Google Scholar 

  37. World Health Organization. (2011). mHealth: New horizons for health through mobile technologies. mHealth: New horizons for health through mobile technologies.

    Google Scholar 

  38. Research2guidance. (2014). m‐Health app developer economics 2014: The state of the art of mHealth app publishing.

    Google Scholar 

  39. Aitken, M., Clancy, B., & Nass, D. (2017). The growing value of digital health: Evidence and impact on human health and the healthcare system. IQVIA Institute for Human Data Science, p 1.

    Google Scholar 

  40. Akpakwu, G. A., Silva, B. J., Hancke, G. P., & Abu-Mahfouz, A. M. (2017). A survey on 5G networks for the internet of things: Communication technologies and challenges. IEEE Access, 6, 3619–3647.

    Article  Google Scholar 

  41. Radhi, A. A. (2015). Data acquisition and controlling system by using mobile phone based microcontroller via bluetooth. Al-Ma’mon College Journal, 26, 319–339.

    Google Scholar 

  42. Wilson, K. (2018). Mobile cell phone technology puts the future of health care in our hands. CMAJ, 190(13), E378–E379.

    Article  Google Scholar 

  43. Covid-19 lockdown 2.0: telemedicine in India to see continued growth, Health News, ET HealthWorld. https://health.economictimes.indiatimes.com/news/health-it/covid-19-lockdown-2–0-telemedicine-in-india-to-see-continued-growth/75172147.

  44. Mackenzie, C. D. (1928). Alexander Graham Bell: The man who contracted space. Boston: Houghton Mifflin.

    Google Scholar 

  45. The First Telephone Call| America’s Library. http://www.americaslibrary.gov/jb/recon/jb_recon_telephone_1.html.

  46. Aronson, S. H. (1977). The Lancet on the telephone 1876–1975. Medical History, 21(1), 69–87.

    Article  Google Scholar 

  47. Graziano, F., Maugeri, R., & Iacopino, D. G. (2015). Telemedicine versus WhatsApp: From tradition to evolution. NeuroReport, 26(10), 602–603.

    Article  Google Scholar 

  48. Thota, R., & Divatia, J. (2015). WhatsApp: What an App! Indian Journal of Critical Care Medicine, 19(6), 363.

    Article  Google Scholar 

  49. Endeley, R. E. (2018). End-to-end encryption in messaging services and national security—case of WhatsApp messenger. Journal of Information Security, 9(01), 95.

    Article  Google Scholar 

  50. WHO Health Alert brings COVID-19 facts to billions via WhatsApp. https://www.who.int/news-room/feature-stories/detail/who-health-alert-brings-covid-19-facts-to-billions-via-whatsapp.

  51. WhatsApp FAQ - IFCN Fact Checking Organizations on WhatsApp. https://faq.whatsapp.com/general/ifc-n-fact-checking-organizations-on-whatsapp.

  52. IFCN Code of Principles. https://ifcncodeofprinciples.poynter.org/signatories.

  53. Healthcare startup MedIoTek’s IoMT device helps prescreen COVID-19 patients. https://yourstory.com/2020/04/coronavirus-healthtech-startup-mediotek-iomt-device-covid-19.

  54. The Growing Role of IoT In COVID-19 Response. https://www.iotforall.com/the-growing-role-of-iot-in-covid-19-response/.

  55. Reed, N. G. (2010). The history of ultraviolet germicidal irradiation for air disinfection. Public Health Reports, 125(1), 15–27.

    Article  MathSciNet  Google Scholar 

  56. How IoT can help fight COVID-19 battle—Geospatial World. https://www.geospatialworld.net/blogs/how-iot-can-help-fight-covid-19-battle/.

  57. Digital thermometer data may provide insight into COVID-19 surges| Healthcare IT News. https://www.healthcareitnews.com/news/digital-thermometer-data-may-provide-insight-covid-19-surges.

  58. Anggorojati, B., & Prasad, R. (2018). Securing communication in the IoT-based health care systems. JurnalIlmuKomputer dan Informasi, 11(1), 1–9.

    Google Scholar 

  59. Alasmari, S., & Anwar, M. (2016, December). Security and privacy challenges in IoT-based health cloud. In 2016 International Conference on Computational Science and Computational Intelligence (CSCI) (pp. 198–201). IEEE.

    Google Scholar 

  60. Ayala, L. (2016). Active medical device cyber-attacks. In Cybersecurity for hospitals and healthcare facilities (pp. 19–37). Apress, Berkeley, CA.

    Google Scholar 

  61. Azmoodeh, A., Dehghantanha, A., Conti, M., & Choo, K. K. R. (2018). Detecting crypto-ransomware in IoT networks based on energy consumption footprint. Journal of Ambient Intelligence and Humanized Computing, 9(4), 1141–1152.

    Article  Google Scholar 

  62. The Growing Value of Digital Health in the United Kingdom—IQVIA. https://www.iqvia.com/insights/the-iqvia-institute/reports/the-growing-value-of-digital-health-in-the-united-kingdom.

  63. What are the Pros and Cons of mHealth?| Mobindustry. https://www.mobindustry.net/does-it-hurt-or-does-it-help-5-pros-and-4-cons-of-mhealth-for-doctors-and-patients/.

  64. IoT in Healthcare: Use cases, trends, advantages and disadvantages| Existek Blog. https://existek.com/blog/iot-in-healthcare/.

  65. http://www.standardsuniversity.org/e-magazine/March-2016/security-and-iot-in-ieee-standards/.

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia, New Delhi, India

    Devansh Sharma, Ali Zaid Bin Nawab & Mansaf Alam

Authors
  1. Devansh Sharma
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ali Zaid Bin Nawab
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mansaf Alam
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Devansh Sharma .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Jamia Millia Islamia (Central University), New Delhi, India

    Khalid Raza

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Sharma, D., Nawab, A.Z.B., Alam, M. (2021). Integrating M-Health with IoMT to Counter COVID-19. In: Raza, K. (eds) Computational Intelligence Methods in COVID-19: Surveillance, Prevention, Prediction and Diagnosis. Studies in Computational Intelligence, vol 923. Springer, Singapore. https://doi.org/10.1007/978-981-15-8534-0_20

Download citation

Publish with us

Policies and ethics

Search

Navigation