Name
Abstract | ||
|---|---|---|
The development of smart environments is cumbersome and time-consuming compared to traditional software, since lacking a standard development process and according tool support. Smart environments are termed "smart" due to pro-active user assistance: User behavior is anticipated by an "intention analysis" software employing machine learning algorithms. In this paper we present a tool that facilitates the development; of intention analysis by guiding the domain expert through the development process. Initially, the tool allows the user-centered design of HCl task models, without taking care of implementation details. Subsequently annotated task models are transformed into low level models, which are applied within the machine learning inference engine. We support; both evaluation at early and later development stages. At early stages we evaluate designed models with expert-generated scenarios to simulate artificial low level sensor data. At later stages we evaluate a physical environment on the basis of real sensor data. A comparison between observed behavior and defined expectation allows identifying usability issues. A close connection between development and evaluation should further ensure rapid software changes and reevaluation to access improvements. |
Year | DOI | Venue |
|---|---|---|
2009 | 10.1007/978-3-642-10263-9_21 | Communications in Computer and Information Science |
DocType | Volume | ISSN |
Conference | 53 | 1865-0929 |
Citations | PageRank | References |
1 | 0.35 | 13 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| christoph burghardt | 1 | 2 | 0.69 |
| stefan propp | 2 | 1 | 0.35 |
| thomas kirste | 3 | 2 | 0.69 |
| Peter Forbrig | 4 | 502 | 80.19 |