Skip to main content

A Real-Time Insulin Injection System

  • Conference paper
Ambient Assisted Living and Active Aging (IWAAL 2013)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8277))

Included in the following conference series:


We develop a prototype for real-time blood sugar control based upon the hypothesis that there is a medical challenge in determining the exact, real-time insulin dose. Our system controls blood sugar by observing the blood sugar level and automatically determining the appropriate insulin dose based on patient’s historical data all in real time. At the heart of our system is an algorithm that determines the appropriate insulin dose. Our algorithm consists of two phases. In the first phase, the algorithm identifies the insulin dose offline using a Markov Process based model. In the other phase, it recursively trains the web hosted Markov model to adapt to different human bodies’ responsiveness.

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

Access this chapter

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Similar content being viewed by others


  1. Akematsu, Y., Tsuji, M.: Economic effect of eHealth: Focusing on the reduction of days spent for treatment. In: 11th International Conference on e-Health Networking, Applications and Services, Healthcom 2009 (2009)

    Google Scholar 

  2. Alasaarela, E., Nemana, R., DeMello, S.: Drivers and challenges of wireless solutions in future healthcare. In: International Conference on eHealth, Telemedicine, and Social Medicine

    Google Scholar 

  3. Andrianasy, F., Milgram, M.: Applying neural networks to adjust insulin-pump doses. In: Proceedings of the 1997 IEEE Workshop Neural Networks for Signal Processing VII (1997)

    Google Scholar 

  4. Campos-Cornejo, F., Campos-Delgado, D.U.: Self-Tuning Insulin Dosing Algorithm for Glucose Regulation in Type 1 Diabetic Patients. In: Pan American Health Care Exchanges, PAHCE 2009 (2009)

    Google Scholar 

  5. Jordanova, M.M.: eHealth: from space medicine to civil healthcare. In: Proceedings of 2nd International Conference on Recent Advances in Space Technologies, RAST 2005 (2005)

    Google Scholar 

  6. King, A.B., Clark, D., Wolfe, G.S.: How much do I give? Dose estimation formulas for once-nightly insulin glargine and premeal insulin lispro in type 1 diabetes mellitus. Endocrine Practice 18(3), 382–386 (2012)

    Article  Google Scholar 

  7. Klonoff, D.C., Buse, J.B., Nielsen, L.L., Guan, X., Bowlus, C.L., Holcombe, J.H., Maggs, D.G.: Exenatide effects on diabetes, obesity, cardiovascular risk factors and hepatic biomarkers in patients with type 2 diabetes treated for at least 3 years. Current Medical Research and Opinion 24(1), 275–286 (2007)

    Google Scholar 

  8. Wang, N., Kang, G.: A monitoring system for type 2 diabetes mellitus. In: IEEE 14th International Conference on e-Health Networking, Applications and Service (Healthcom)

    Google Scholar 

  9. Rizza, R.A., Mandarino, L.J., Gerich, J.E.: Dose-response characteristics for effects of insulin on production and utilization of glucose in man. American Journal of Physiology-Endocrinology and Metabolism 240(6), E630–E639 (1981)

    Google Scholar 

  10. Ross, S.M.: Introduction to Probability Models, 10th edn. Elsevier AP (2010)

    Google Scholar 

  11. Shimauchi, T., Kugai, N., Nagata, N., Takatani, O.: Microcomputer-aided insulin dose determination in intensified conventional insulin therapy. IEEE Transactions on Biomedical Engineering (2013)

    Google Scholar 

  12. Stein, O.S., Eirik, A., Ragnar, M.J., Fred, G.: Statistical Modeling of Aggregated Lifestyle and Blood Glucose Data in Type 1 Diabetes Patients. In: Second International Conference on eHealth, Telemedicine, and Social Medicine (2010)

    Google Scholar 

  13. Taha, H.A.: Operations Research: An Introduction, 9th edn. Prentice Hall, New Jersey (2010)

    Google Scholar 

  14. Pickup, J.C.: Insulin-pump therapy for type 1 diabetes mellitus. New England Journal of Medicine 366(17), 1616–1624 (2012)

    Article  Google Scholar 

  15. (accessed on July 2013)

  16. Vasilyeva, E., Pechenizkiy, M., Puuronen, S.: Towards the framework of adaptive user interfaces for eHealth. In: Proceedings of 18th IEEE Symposium on Computer-Based Medical Systems (2005)

    Google Scholar 

  17. Wallace, T.M., Matthews, D.R.: The assessment of insulin resistance in man. Diabetic Medicine 19(7), 527–534 (2002)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations


Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer International Publishing Switzerland

About this paper

Cite this paper

Otoom, M., Alshraideh, H., Almasaeid, H.M., López-de-Ipiña, D., Bravo, J. (2013). A Real-Time Insulin Injection System. In: Nugent, C., Coronato, A., Bravo, J. (eds) Ambient Assisted Living and Active Aging. IWAAL 2013. Lecture Notes in Computer Science, vol 8277. Springer, Cham.

Download citation

  • DOI:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03091-3

  • Online ISBN: 978-3-319-03092-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics