Name
Title | ||
|---|---|---|
Improving Wearable Activity Recognition via Fusion of Multiple Equally-Sized Data Subwindows. |
Abstract | ||
|---|---|---|
The automatic recognition of physical activities typically involves various signal processing and machine learning steps used to transform raw sensor data into activity labels. One crucial step has to do with the segmentation or windowing of the sensor data stream, as it has clear implications on the eventual accuracy level of the activity recogniser. While prior studies have proposed specific window sizes to generally achieve good recognition results, in this work we explore the potential of fusing multiple equally-sized subwindows to improve such recognition capabilities. We tested our approach for eight different subwindow sizes on a widely-used activity recognition dataset. The results show that the recognition performance can be increased up to 15% when using the fusion of equally-sized subwindows compared to using a classical single window. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-20521-8_30 | ADVANCES IN COMPUTATIONAL INTELLIGENCE, IWANN 2019, PT I |
Keywords | Field | DocType |
Activity recognition,Segmentation,Data window,Data fusion,Wearable sensors | Signal processing,Activity recognition,Pattern recognition,Segmentation,Data stream,Computer science,Wearable computer,Fusion,Sensor fusion,Artificial intelligence | Conference |
Volume | ISSN | Citations |
11506 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oresti Baños | 1 | 380 | 35.57 |
| Juan Manuel Galvez | 2 | 9 | 1.84 |
| M. Damas | 3 | 387 | 33.04 |
| Alberto Guillén | 4 | 0 | 0.34 |
| Luis Javier Herrera | 5 | 129 | 23.29 |
| Héctor Pomares | 6 | 651 | 64.11 |
| I. Rojas | 7 | 1750 | 143.09 |
| Claudia Villalonga | 8 | 81 | 8.26 |