Abstract
This paper presents an advanced long-range low-power Internet of Things wearable temperature sensor to evaluate and predict the likelihood of a heart failure event in high-risk patients. Initial trials have validated the potential of long-range long-term personalized community-based monitoring with smart intervention decision making. The intelligent device implements machine learning to understand the user’s activities of Daily Living (ADL) and their environment; using this information coupled with their body temperature allows the system to evaluate and predict the likelihood of a heart failure event. The solution is based upon the European 868 MHz LoRaWAN standard. As Ulster University roll out a regional LoRaWAN “Things Connected” network across Northern Ireland (owned by Digital Catapult, UK) the embryonic solution will be tested on a larger scale for both home based monitoring as well as patients undertaking daily living activities.
Original language | English |
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Pages | 1-4 |
Number of pages | 4 |
Publication status | Published (in print/issue) - 6 Jul 2018 |
Event | British HCI Conference 2018 - Belfast, Belfast, Northern Ireland Duration: 2 Jul 2018 → 6 Jul 2018 |
Conference
Conference | British HCI Conference 2018 |
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Abbreviated title | BHCI2018 |
Country/Territory | Northern Ireland |
City | Belfast |
Period | 2/07/18 → 6/07/18 |
Keywords
- Cardiac
- Heart Failure
- Internet of Things
- LPWAN,
- Machine learning
- Intelligent
- Intervention