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). Article Number 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) (KAR id:65856)
| 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. | |
| 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: | Divisions > Division of Computing, Engineering and Mathematical Sciences > School of Computing |
| Depositing User: | Ozgur Kafali |
| Date Deposited: | 02 Feb 2018 14:31 UTC |
| Last Modified: | 05 Nov 2024 11:04 UTC |
| Resource URI: | https://kar.kent.ac.uk/id/eprint/65856 (The current URI for this page, for reference purposes) |
University of Kent Author Information
Kafalı, Özgur.
| Creator's ORCID: |
 https://orcid.org/0000-0001-9296-2087
|
|---|---|
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