Name
Abstract | ||
|---|---|---|
Remote patient monitoring is essential for many patients that are suffering from acute diseases such as different heart conditions. Continuous health monitoring can provide medical services that consider the current medical state of the patient and to predict or early-detect future potentially critical situations. In this regard, Internet of Things as a multidisciplinary paradigm can provide profound impacts. However, the current IoT-based systems may encounter difficulties to provide continuous and real time patient monitoring due to issues in data analytics. In this paper, we introduce a new IoT-based approach to offer smart medical warning in personalized patient monitoring. The proposed approach consider local computing paradigm enabled by machine learning algorithms and automate management of system components in computing section. The proposed system is evaluated via a case study concerning continuous patient monitoring to early-detect patient deterioration via arrhythmia in ECG signal. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IWBIS.2016.7872884 | 2016 International Workshop on Big Data and Information Security (IWBIS) |
Keywords | DocType | ISBN |
Internet of Things,Fog Comouting,Patient monitoring,Autonomic computing,machine learning | Conference | 978-1-5090-3478-9 |
Citations | PageRank | References |
1 | 0.39 | 0 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Iman Azimi | 1 | 50 | 5.99 |
| Arman Anzanpour | 2 | 38 | 5.93 |
| Amir Masoud Rahmani | 3 | 787 | 110.98 |
| Pasi Liljeberg | 4 | 1147 | 92.79 |
| Tapio Salakoski | 5 | 1513 | 106.70 |