title: Activity Recognition for Diabetic Patients Using a Smartphone.
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.
authors.affiliation: Jožef Stefan Institue, Jamova cesta 39, Slovenia. boza.cvetkovic@ijs.si.|Jožef Stefan International Postgraduate School, Jamova cesta 39, Slovenia. boza.cvetkovic@ijs.si.
authors.forename: Božidara
authors.identifier: http://orcid.org/0000-0003-2711-1280
authors.initials: B
authors.lastname: Cvetković
authors.affiliation: Jožef Stefan Institue, Jamova cesta 39, Slovenia.|Jožef Stefan International Postgraduate School, Jamova cesta 39, Slovenia.
authors.forename: Vito
authors.initials: V
authors.lastname: Janko
authors.affiliation: Royal Holloway, University of London, Egham, TW20 0EX, UK.
authors.forename: Alfonso E
authors.initials: AE
authors.lastname: Romero
authors.affiliation: North Carolina State University, Raleigh, NC, 27695-8206, USA.
authors.forename: Özgür
authors.initials: Ö
authors.lastname: Kafalı
authors.affiliation: Royal Holloway, University of London, Egham, TW20 0EX, UK.
authors.forename: Kostas
authors.initials: K
authors.lastname: Stathis
authors.affiliation: Jožef Stefan Institue, Jamova cesta 39, Slovenia.|Jožef Stefan International Postgraduate School, Jamova cesta 39, Slovenia.
authors.forename: Mitja
authors.initials: M
authors.lastname: Luštrek
mesh_terms: D061725:Accelerometry / Q000295:instrumentation*; D000465:Algorithms; D003920:Diabetes Mellitus / Q000503:physiopathology*; D004562:Electrocardiography; D040362:Geographic Information Systems; D006801:Humans; D000069550:Machine Learning; D018670:Monitoring, Ambulatory / Q000379:methods*; D009043:Motor Activity / Q000502:physiology*; D000068997:Smartphone
keywords: Activity recognition; Diabetes; Lifestyle; Smartphone