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      LPWAN Wearable Intelligent Healthcare Monitoring for Heart Failure Prevention

      , , ,

      Proceedings of the 32nd International BCS Human Computer Interaction Conference (HCI)

      Human Computer Interaction Conference

      4 - 6 July 2018

      Cardiac, Heart failure, Intelligent, Intervention, Internet of Things, LPWAN, Machine learning

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          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.

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          Most cited references 14

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          Long terms trends of multimorbidity and association with physical activity in older English population

          Background Multimorbidity has become one of the main challenges in the recent years for patients, health care providers and the health care systems globally. However, literature describing the burden of multimorbidity in the elderly population, especially longitudinal trends is very limited. Physical activity is recommended as one of the main lifestyle changes in the prevention and management of multiple chronic diseases worldwide; however, the evidence on its association with multimorbidity remains inconclusive. Therefore, we aimed to assess the longitudinal trends of multimorbidity and the association between multimorbidity and physical activity in a nationally representative cohort of the English population aged ≥50 years between 2002 and 2013. Methods We used data on 15,688 core participants from six waves of the English Longitudinal Study of Ageing, with complete information on physical activity. Self-reported physical activity was categorised as inactive, mild, moderate and vigorous levels of physical activity. We calculated the number of morbidities and the prevalence of multimorbidity (more than 2 chronic conditions) between 2002 and 2013 overall and by levels of self-reported physical activity. We estimated the odds ratio (OR) and 95 % confidence intervals (CI) for multimorbidity by each category of physical activity, adjusting for potential confounders. Results There was a progressive decrease over time in the proportion of participants without any chronic conditions (33.9 % in 2002/2003 vs. 26.8 % in 2012/2013). In contrast, the prevalence of multimorbidity steadily increased over time (31.7 % in 2002/2003 vs. 43.1 % in 2012/2013). Compared to the physically inactive group, the OR for multimorbidity was 0.84 (95 % CI 0.78 to 0.91) in mild, 0.61 (95 % CI 0.56 to 0.66) in moderate and 0.45 (95 % CI 0.41 to 0.49) in the vigorous physical activity group. Conclusion This study demonstrated an inverse dose-response association between levels of physical activity and multimorbidity, however, given the increasing prevalence of multimorbidity over time, there is a need to explore causal associations between physical activity and multimorbidity and its impact as a primary prevention strategy to prevent the occurrence of chronic conditions later in life and reduce the burden of multimorbidity. Electronic supplementary material The online version of this article (doi:10.1186/s12966-016-0330-9) contains supplementary material, which is available to authorized users.
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            TensorFlow: A System for Large-Scale Machine Learning

             M Abadi,  P Barham,  J. Chen (2016)
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              Applied Machine Learning at Facebook: A Datacenter Infrastructure Perspective

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                Author and article information

                Contributors
                Conference
                July 2018
                July 2018
                : 1-4
                Affiliations
                School of Engineering

                Ulster University

                Shore Rd., N’abbey

                Co.Antrim, BT370QB
                Sch. of Computing & Mathematics

                Ulster University

                Shore Rd., N’abbey

                Co.Antrim, BT370QB
                CHIC Innovation Centre

                Ulster University

                Shore Rd., N’abbey

                Co.Antrim, BT370QB
                Article
                10.14236/ewic/HCI2018.126
                © Catherwood et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2018. Belfast, UK.

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of the 32nd International BCS Human Computer Interaction Conference
                HCI
                32
                Belfast, UK
                4 - 6 July 2018
                Electronic Workshops in Computing (eWiC)
                Human Computer Interaction Conference
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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