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.
|Number of pages||4|
|Publication status||Published - 6 Jul 2018|
|Event||British HCI Conference 2018 - Belfast, Belfast, Northern Ireland|
Duration: 2 Jul 2018 → 6 Jul 2018
|Conference||British HCI Conference 2018|
|Period||2/07/18 → 6/07/18|
- Heart Failure
- Internet of Things
- Machine learning
Catherwood, P., Rafferty, J., McComb, S., & McLaughlin, J. (2018). LPWAN Wearable Intelligent Healthcare Monitoring for Heart Failure Prevention. 1-4. Paper presented at British HCI Conference 2018, Belfast, Northern Ireland.