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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

Abstract

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.

Keywords

Activity recognition Smartphone Lifestyle Diabetes 

Notes

Acknowledgments

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 (www.commodity12.eu).

Supplementary material

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

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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

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