Name
Abstract | ||
|---|---|---|
We propose a context-free semantic localisation approach to visualise and analyse indoor movements. We focus on settings where indoor location or rooms have strongly associated semantics, such as hospitals. We describe an approach that can work with different localisation systems, with little knowledge of the physical space properties, and with minimal bootstrapping required. We propose a movement representation that consists of time-encoded strings, and discuss how our approach can be used for analysing and visualising longitudinal indoor localisation data.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341162.3349329 | Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers |
Keywords | Field | DocType |
indoor localisation, localisation analytics | Computer vision,Computer science,Bootstrapping,Human–computer interaction,Artificial intelligence,Physical space,Semantics | Conference |
ISBN | Citations | PageRank |
978-4503-6869-8 | 0 | 0.34 |
References | Authors | |
20 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gabriele Marini | 1 | 8 | 2.16 |
| Goncalves, J. | 2 | 404 | 42.24 |
| Eduardo Velloso | 3 | 400 | 32.81 |
| Raja Jurdak | 4 | 1069 | 92.45 |
| Vassilis Kostakos | 5 | 1718 | 138.50 |