Name
Abstract | ||
|---|---|---|
The automatic and unobtrusive identification of user activities is a challenging goal in human behavior analysis. The physical activity that a user exhibits can be used as contextual data, which can inform applications that reside in public spaces. In this paper, we focus on wearable inertial sensors to recognize physical activities. Feature set evaluation for 5 typical activities is performed by measuring accuracy for combinations of 6 often-used features on a set of II well-known classifiers. To verify significance of this analysis, a t-test evaluation was performed for every combination of these feature subsets. We identify an easy-to-compute feature set, which has given us significant results and at the same time utilizes a minimum of resources. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-31479-7_17 | Communications in Computer and Information Science |
Keywords | Field | DocType |
activity recognition | Computer vision,Activity recognition,Wearable computer,Computer science,Contextual design,Support vector machine,Feature set,Fast Fourier transform,Artificial intelligence,Inertial measurement unit | Conference |
Volume | ISSN | Citations |
277 | 1865-0929 | 3 |
PageRank | References | Authors |
0.39 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Syed Agha Muhammad | 1 | 7 | 1.12 |
| Bernd Niklas Klein | 2 | 27 | 3.43 |
| K Van Laerhoven | 3 | 1083 | 185.94 |
| Klaus David | 4 | 269 | 34.41 |