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Activity recognition for diabetic patients using a smartphone

Cvetkovic, Bozidara, Janko, Vito, Romero, Alfonso E, Kafalı, Özgur, Stathis, Kostas, Lustrek, Mitja (2016) Activity recognition for diabetic patients using a smartphone. Journal of medical systems, 40 (12). p. 256. ISSN 0148-5598. (doi:10.1007/s10916-016-0598-y) (The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided)

The full text of this publication is not currently available from this repository. You may be able to access a copy if URLs are provided. (Contact us about this Publication)
Official URL
https://doi.org/10.1007/s10916-016-0598-y

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.

Item Type: Article
DOI/Identification number: 10.1007/s10916-016-0598-y
Uncontrolled keywords: Activity recognition, Smartphone, Lifestyle, Diabetes
Subjects: Q Science > Q Science (General) > Q335 Artificial intelligence
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76.E95 Expert Systems (Intelligent Knowledge Based Systems)
Divisions: Faculties > Sciences > School of Computing
Depositing User: Ozgur Kafali
Date Deposited: 02 Feb 2018 14:31 UTC
Last Modified: 24 Jul 2019 10:17 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65856 (The current URI for this page, for reference purposes)
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