Context-aware support for cardiac health monitoring using federated machine learning
Ogbuabor, Godwin, Augusto, Juan Carlos 
ORCID: https://orcid.org/0000-0002-0321-9150, Moseley, Ralph ORCID: https://orcid.org/0000-0001-5504-0665 and van Wyk, Aléchia ORCID: https://orcid.org/0000-0001-6823-088X
(2021)
Context-aware support for cardiac health monitoring using federated machine learning.
Bramer, Max and Ellis, Richard, eds.
Artificial Intelligence XXXVIII: 41st SGAI International Conference on Artificial Intelligence, AI 2021, Cambridge, UK, December 14–16, 2021, Proceedings.
In: 41st SGAI International Conference on Artificial Intelligence (AI-2021), 14-16 Dec 2021, Cambridge, England.
pbk-ISBN 9783030910990, e-ISBN 9783030911003.
ISSN 0302-9743
[Conference or Workshop Item]
(doi:10.1007/978-3-030-91100-3_22)
|
PDF
- Final accepted version (with author's formatting)
Download (2MB) | Preview |
Abstract
Context-awareness provides a platform for healthcare professionals to assess the health status of patients in their care using multiple relevant parameters such as heart rate, electrocardiogram (ECG) signals and activity data. It involves the use of digital technologies to monitor the health condition of a patient in an intelligent environment. Feedback gathered from relevant professionals at earlier stages of the project indicates that physical activity recognition is an essential part of cardiac condition monitoring. However, the traditional machine learning method f developing a model for activity recognition suffers two significant challenges; model overfitting and privacy infringements. This research proposes an intelligent and privacy-oriented context-aware decision support system for cardiac health monitoring using the physiological and the activity data of the patient. The system makes use of a federated machine learning approach to develop a model for physical activity recognition. Experimental analysis shows that the federated approach has advantages over the centralized approach in terms of model generalization whilst maintaining the privacy of the user.
| Item Type: | Conference or Workshop Item (Paper) |
|---|---|
| Research Areas: | A. > School of Science and Technology > Computer Science > Intelligent Environments group |
| Item ID: | 33875 |
| Notes on copyright: | This version of the contribution has been accepted for publication, after peer review (when applicable) but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/978-3-030-91100-3_22 Use of this Accepted Version is subject to the publisher’s Accepted Manuscript terms of use https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms |
| Useful Links: | |
| Depositing User: | Juan Augusto |
| Date Deposited: | 05 Oct 2021 16:39 |
| Last Modified: | 06 Dec 2022 04:04 |
| URI: | https://eprints.mdx.ac.uk/id/eprint/33875 |
Actions (login required)
 |
View Item |
Statistics
Additional statistics are available via IRStats2.
CORE (COnnecting REpositories)
CORE (COnnecting REpositories)