Name
Abstract | ||
|---|---|---|
We outline a system enabling accurate remote assessment of stroke rehabilitation levels using wrist worn accelerometer time series data. The system is built based on features generated from clustering models across sliding windows in the data and makes use of computation in the cloud. Predictive models are built using advanced machine learning techniques. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/eScience.2018.00063 | 2018 IEEE 14th International Conference on e-Science (e-Science) |
Keywords | Field | DocType |
connected healthcare,cloud computing,machine learning,applied statistical inference,smartwatch,accelerometer | Rehabilitation,Data mining,Time series,Data modeling,Microsoft Windows,Accelerometer,Computer science,Wearable computer,Real-time computing,Cluster analysis,Cloud computing | Conference |
ISSN | ISBN | Citations |
2325-372X | 978-1-5386-9157-1 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shane Halloran | 1 | 41 | 2.00 |
| Jianqing Shi | 2 | 1 | 3.07 |
| Yu Guan | 3 | 195 | 22.59 |
| Xi Chen | 4 | 226 | 49.58 |
| Michael Dunne-Willows | 5 | 0 | 0.34 |
| Janet A. Eyre | 6 | 8 | 2.32 |