Name
Abstract | ||
|---|---|---|
In order to act intelligently, a smart environment needs to have a notion about its users. Hidden Markov models are especially suited to recognize for example the state of a meeting in a smart meeting room, as they can cope with the noisy and intermittent sensor values. However, modeling the user behavior as an HMM is challenging, because of the high degrees of freedom the users have when acting in such a smart environment. Therefore, we compare two methods that ease the automatic generation of HMM and express the human behavior. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-02809-0_22 | HCI (11) |
Keywords | Field | DocType |
intermittent sensor value,smart environment,high degree,modeling human behavior,human behavior,smart environments,probabilistic approach,hidden markov model,user behavior,automatic generation,smart meeting room,degree of freedom | Smart environment,Artificial intelligence,Probabilistic logic,Engineering,Hidden Markov model,Machine learning | Conference |
Volume | ISSN | Citations |
5620 | 0302-9743 | 1 |
PageRank | References | Authors |
0.35 | 7 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christoph Burghardt | 1 | 29 | 3.35 |
| Thomas Kirste | 2 | 93 | 18.37 |