Name
Title | ||
|---|---|---|
A novel method for the automatic segmentation of activity data from a wrist worn device: Preliminary results. |
Abstract | ||
|---|---|---|
Activity monitoring is used in a number of fields in order to assess the physical activity of the user. Applications include health and well-being, rehabilitation and enhancing independent living. Data are often gathered from multiple accelerometers and analysis focuses on multi-parametric classification. For longer term monitoring this is unsuitable and it is desirable to develop a method for the precise analysis of activity data with respect to time. This paper presents the initial results of a novel approach to this problem which is capable of segmenting activity data collected from a single accelerometer recording naturalized activity. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/EMBC.2014.6944864 | EMBC |
Keywords | Field | DocType |
independent living,health,biomedical measurement,wrist worn device,multiparametric classification,patient monitoring,naturalized activity,single accelerometer,medical signal processing,well-being,rehabilitation,automatic segmentation,multiple accelerometers,body sensor networks,activity data analysis,patient rehabilitation,signal classification,accelerometers,activity monitoring,physical activity | Computer vision,Wrist,Segmentation,Computer science,Artificial intelligence | Conference |
Volume | ISSN | Citations |
2014 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 1 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| James D. Amor | 1 | 2 | 1.73 |
| Vijayalakshmi Ahanathapillai | 2 | 0 | 0.34 |
| Christopher J James | 3 | 0 | 0.68 |