Journal of Medical Systems

, 40:256 | Cite as

Activity Recognition for Diabetic Patients Using a Smartphone

  • Božidara Cvetković
  • Vito Janko
  • Alfonso E. Romero
  • Özgür Kafalı
  • Kostas Stathis
  • Mitja Luštrek
Mobile Systems
Part of the following topical collections:
  1. Personal Health Systems for Chronic Diseases Monitoring


Diabetes is a disease that has to be managed through appropriate lifestyle. Technology can help with this, particularly when it is designed so that it does not impose an additional burden on the patient. This paper presents an approach that combines machine-learning and symbolic reasoning to recognise high-level lifestyle activities using sensor data obtained primarily from the patient’s smartphone. We compare five methods for machine-learning which differ in the amount of manually labelled data by the user, to investigate the trade-off between the labelling effort and recognition accuracy. In an evaluation on real-life data, the highest accuracy of 83.4 % was achieved by the MCAT method, which is capable of gradually adapting to each user.


Activity recognition Smartphone Lifestyle Diabetes 



This paper significantly extends the work of Luštrek et al. [20], which was presented in the PHSCD 2015 Workshop. The work was partially supported by the EU FP7 project COMMODITY12 (

Supplementary material

10916_2016_598_MOESM1_ESM.pdf (419 kb)
(PDF 419 KB)


  1. 1.
    Amft, O., Ambient, on-body, and implantable monitoring technologies to assess dietary behavior. Handbook of Behavior, Food and Nutrition. Springer 2011.Google Scholar
  2. 2.
    Commodity12, 2016.
  3. 3.
    Diabetes Atlas, 2016.
  4. 4.
    Cvetković, B., Milić, R., and Luštrek, M.: Estimating energy expenditure with multiple models using different wearable sensors. IEEE J. Biomed. Health Inf. 20(4):1081–1087, 2016.Google Scholar
  5. 5.
    Cvetković, B., Janko, V., and Luštrek, M., Demo abstract: Activity recognition and human energy expenditure estimation with a smartphone. In: PerCom 2015, pp. 23–27. St. Louis, USA, 2015.Google Scholar
  6. 6.
    Cvetković, B., Mirchevska, V., Janko, V., and Luštrek, M., Recognition of high-level activities with a smartphone. In: Adjunct Proceedings of the 2015 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2015 ACM International Symposium on Wearable Computers (UbiComp/ISWC’15 Adjunct), pp. 1453–1461, 2015.Google Scholar
  7. 7.
    Cvetković, B., Kaluža, B., Gams, M., and Luštrek, M., Adapting activity recognition to a person with Multi-Classifier Adaptive Training. Journal of Ambient Intelligence and Smart Environments 7(2):171–185, 2015.Google Scholar
  8. 8.
    Dernbach, S., Das, B., Krishnan, N. C., Thomas, B. L., and Cook, D. J., Simple and complex activity recognition through smart phones, 2012.Google Scholar
  9. 9.
    Foursquare API., 2016.
  10. 10.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., and Witten, I. H., The WEKA data mining software: An update. SIGKDD Explorations 11(1):10–18, 2009. doi: 10.1145/1656274.1656278.
  11. 11.
    Helal, A., Cook, D. J., and Schmalz, M., Smart home-based health platform for behavioral monitoring and alteration of diabetes patients. Journal of Diabetes Science and Technology 3(1):141–148, 2009.CrossRefPubMedPubMedCentralGoogle Scholar
  12. 12.
    jAudio library. (2016)
  13. 13.
    Kafalı, Ö, Bromuri, S., Sindlar, M., Van der Weide, T., Pelaez, E. A., Schaechtle, U., Alves, B., Zufferey, D., Rodriguez-Villegas, E., Schumacher, M. I., and Stathis, K., COMMODITY12: A smart e-health environment for diabetes management. Journal of Ambient Intelligence and Smart Environments 5(5):479–502, 2013. doi: 10.3233/AIS-130220.
  14. 14.
    Kafalı, Ö., Alfonso, E.R., and Stathis, K., Activity Recognition for an Agent-oriented Personal Health System. Principles and Practice of Multi-Agent Systems – 17th International Conference, PRIMA 2014, Springer, 2014.Google Scholar
  15. 15.
    Kafalı, Ö., Schaechtle, U., and Stathis, K., Hydra: A hybrid diagnosis and monitoring architecture for diabetes. 16th IEEE International Conference on e-Health Networking, Applications and Services, Healthcom 2014, pp. 531–536. Natal-RN, Brazil, 2014.Google Scholar
  16. 16.
    Kowalski, R., and Sergot, M., A logic-based calculus of events. New Generation Computing 4(1):67–95, 1986.CrossRefGoogle Scholar
  17. 17.
    Lara, O. D., and Labrador, M. A., A Survey on Human Activity Recognition using Wearable Sensors. IEEE Communications Surveys & Tutorials 15(3):1192–1209, 2013.CrossRefGoogle Scholar
  18. 18.
    Lee, Z. S., and Cho, S. B., Activity recognition using hierarchical Hidden Markov Models on a smartphone with 3D accelerometer, pp. 460-467. Berlin Heidelberg: Hybrid Artificial Intelligent Systems, Springer, 2011.Google Scholar
  19. 19.
    Lin, L., Location-Based Activity Recognition. Ph.D. Dissertation University of Washington, 2006.Google Scholar
  20. 20.
    Luštrek, M., Cvetković, B., Mirchevska, V., Kafalı, Ö., Romero, A., and Stathis, K., Recognising lifestyle activities of diabetic patients with a smartphone. Pervasive Health 2015: Workshop on Personal Health Systems for Chronic Diseases, (PHSCD 2015).Google Scholar
  21. 21.
    Wang, Y., Lin, J., Annavaram, M., Jacobson, Q. A., Hong, J., Krishnamachari, B., and Sadeh, N., A framework of energy efficient mobile sensing for automatic user state recognition. In: Proceedings of the 7th International Conference on Mobile Systems, Applications, and Services, pp. 179–192. ACM, New York, USA, 2009.Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Jožef Stefan InstitueJamova cesta 39Slovenia
  2. 2.Jožef Stefan International Postgraduate SchoolJamova cesta 39Slovenia
  3. 3.North Carolina State UniversityRaleighUSA
  4. 4.Royal HollowayUniversity of LondonEghamUK

Personalised recommendations