Dénes-Fazakas, Lehel and Szilágyi, László and Tasic, Jelena and Kovács, Levente and Eigner, György (2021) Detection of physical activity using machine learning methods. In: 20th IEEE International Symposium on Computational Intelligence and Informatics, 2020.11.05. - 2020.11.07., Budapest, Hungary.
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Abstract
In the case of diabetes mellitus physical activity does have a high effect on the glycemic state of the patients. This is especially regarding the patients with Type 1 diabetes mellitus, who need external insulin administration in their daily life. Nevertheless, physical activity - as one source of stress - is underrepresented in the decisions of patients and medical staff and in the decisions of the available automated glucose regulatory devices. The goal of the study was to build up a simulation framework for data generation and to assess which machine learning solution can be the most accurate in the identification of physical activity.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Subjects: | R Medicine / orvostudomány > RC Internal medicine / belgyógyászat > RC658.5 Diabetes / diabetológia T Technology / alkalmazott, műszaki tudományok > T2 Technology (General) / műszaki tudományok általában |
| Depositing User: | Dr Dániel András Drexler |
| Date Deposited: | 19 Feb 2021 12:56 |
| Last Modified: | 03 Apr 2023 07:08 |
| URI: | http://real.mtak.hu/id/eprint/121321 |
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