Name
Abstract | ||
|---|---|---|
In this paper, we present a computational state space model to track and analyse activities of workers during manual assembly processes. Those models are well suited to capture the semi-structured processes present in final product assembly tasks. But in contrast to pure activity recognition systems, which map sensor data to executed activities, those models are able to track the context of the user, and to reason about context variables which are not directly observable through sensors. We describe our modelling approach and report on first evaluation results. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1145/2790044.2790058 | iWOAR |
Field | DocType | Citations |
Observable,Activity recognition,Final product,State-space representation,MAP sensor,Artificial intelligence,Engineering,Machine learning | Conference | 1 |
PageRank | References | Authors |
0.37 | 4 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sebastian Bader | 1 | 51 | 14.66 |
| Frank Krüger | 2 | 8 | 2.57 |
| Thomas Kirste | 3 | 76 | 9.35 |