Name
Abstract | ||
|---|---|---|
Ensuring ubiquity, robustness and continuity of monitoring is of key importance in activity recognition. To that end, multiple sensor configurations and fusion techniques are ever more used. In this paper we present a multi-sensor meta-classifier that aggregates the knowledge of several sensor-based decision entities to provide a unique and reliable activity classification. This model introduces a new weighting scheme which improves the rating of the impact that each entity has on the decision fusion process. Sensitivity and specificity are particularly considered as insertion and rejection weighting metrics instead of the overall accuracy classification performance proposed in a previous work. For the sake of comparison, both new and previous weighting models together with feature fusion models are tested on an extensive activity recognition benchmark dataset. The results demonstrate that the new weighting scheme enhances the decision aggregation thus leading to an improved recognition system. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1007/978-3-642-38682-4_24 | IWANN (2) |
Keywords | Field | DocType |
extensive activity recognition benchmark,previous weighting model,improved recognition system,multi-sensor meta-classifier,activity recognition,rejection weighting metrics,feature fusion model,decision fusion process,decision aggregation,new weighting scheme,fusion technique | Feature fusion,Weighting,Activity recognition,Decision fusion,Pattern recognition,Recognition system,Computer science,Robustness (computer science),Artificial intelligence,Classifier (linguistics),Wireless sensor network,Machine learning | Conference |
Volume | ISSN | Citations |
7903 | 0302-9743 | 7 |
PageRank | References | Authors |
0.45 | 16 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Oresti Baños | 1 | 380 | 35.57 |
| M. Damas | 2 | 387 | 33.04 |
| Héctor Pomares | 3 | 651 | 64.11 |
| I. Rojas | 4 | 1750 | 143.09 |