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Recognising lifestyle activities of diabetic patients with a smartphone

Luštrek, Mitja, Cvetkovic, Bozidara, Mirchevska, Violeta, Kafalı, Özgür, Romero, Alfonso, Stathis, Kostas (2015) Recognising lifestyle activities of diabetic patients with a smartphone. EAI Endorsed Transactions on Pervasive Health and Technology, 15 (4). ISSN 2411-7145. (doi:10.4108/icst.pervasivehealth.2015.259118) (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
http://dx.doi.org/10.4108/icst.pervasivehealth.201...

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 lifestyle-monitoring 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.

Item Type: Article
DOI/Identification number: 10.4108/icst.pervasivehealth.2015.259118
Uncontrolled keywords: diabetes, lifestyle, activity recognition, smartphone, sensors
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: 04 Feb 2018 19:52 UTC
Last Modified: 07 Oct 2019 13:55 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/65891 (The current URI for this page, for reference purposes)
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