phat 15(4): e4

Research Article

Recognising lifestyle activities of diabetic patients with a smartphone

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  • @ARTICLE{10.4108/icst.pervasivehealth.2015.259118,
        author={Mitja Luštrek and Bozidara Cvetkovic and Violeta Mirchevska and \O{}zg\'{y}r Kafalı and Alfonso Romero and Kostas Stathis},
        title={Recognising lifestyle activities of diabetic patients with a smartphone},
        journal={EAI Endorsed Transactions on Pervasive Health and Technology},
        volume={1},
        number={4},
        publisher={EAI},
        journal_a={PHAT},
        year={2015},
        month={8},
        keywords={diabetes, lifestyle, activity recognition, smartphone, sensors},
        doi={10.4108/icst.pervasivehealth.2015.259118}
    }
    
  • Mitja Luštrek
    Bozidara Cvetkovic
    Violeta Mirchevska
    Özgür Kafalı
    Alfonso Romero
    Kostas Stathis
    Year: 2015
    Recognising lifestyle activities of diabetic patients with a smartphone
    PHAT
    EAI
    DOI: 10.4108/icst.pervasivehealth.2015.259118
Mitja Luštrek1, Bozidara Cvetkovic1,*, Violeta Mirchevska1, Özgür Kafalı2, Alfonso Romero2, Kostas Stathis2
  • 1: Jožef Stefan Institute
  • 2: Royal Holloway, University of London
*Contact email: boza.cvetkovic@ijs.si

Abstract

Diabetes is both heavily affected by the patients’ lifestyle, and it affects their lifestyle. Most diabetic patients can manage the disease without technological assistance, so we should not burden them with technology unnecessarily, but lifestylemonitoring technology can still be beneficial both for patients and their physicians. Because of that we developed an approach to lifestyle monitoring that uses the smartphone, which most patients already have. The approach consists of three steps. First, a number of features are extracted from the data acquired by smartphone sensors, such as the user’s location from GPS coordinates and visible wi-fi access points, and the physical activity from accelerometer data. Second, several classifiers trained by machine learning are used to recognise the user’s activity, such as work, exercise or eating. And third, these activities are refined by symbolic reasoning encoded in Event Calculus. The approach was trained and tested on five people who recorded their activities for two weeks each. Its classification accuracy was 0.88.