Name
Abstract | ||
|---|---|---|
The reconstruction of human activities is an important prerequisite to provide assistance. In this paper, we present an activity and plan recognition approach which is based on causal models of human activities. We show, that it is possible to estimate current activities, the underlying goal of the user, and context information about the state of the environment from noisy sensor data. Therefore we use real world data obtained from a smart home system while observing unrestricted activities of daily living in an inhabited flat. We evaluate the accuracy of the recognition for simulated data of different granularity and data obtained from the smart home system. We furthermore show that performance measures solely based on action sequences are not sufficient to evaluate a recognition system. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005756804300437 | ICAART |
Field | DocType | Citations |
Data mining,Everyday life,Activities of daily living,Activity recognition,State of the Environment,Computer science,Home automation,Artificial intelligence,Plan recognition,Granularity,Machine learning,Causal model | Conference | 2 |
PageRank | References | Authors |
0.42 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Max Schröder | 1 | 4 | 5.86 |
| Sebastian Bader | 2 | 51 | 14.66 |
| Frank Krüger | 3 | 53 | 10.43 |
| Thomas Kirste | 4 | 117 | 25.44 |