Name
Title | ||
|---|---|---|
Complex activity recognition using context driven activity theory in home environments |
Abstract | ||
|---|---|---|
This paper proposes a context driven activity theory (CDAT) and reasoning approach for recognition of concurrent and interleaved complex activities of daily living (ADL) which involves no training and minimal annotation during the setup phase. We develop and validate our CDAT using the novel complex activity recognition algorithm on two users for three weeks. The algorithm accuracy reaches 88.5% for concurrent and interleaved activities. The inferencing of complex activities is performed online and mapped onto situations in near real-time mode. The developed systems performance is analyzed and its behavior evaluated. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-22875-9_4 | NEW2AN |
Keywords | Field | DocType |
activity recognition | Activity recognition,Annotation,Activities of daily living,Psychology,Context awareness,Artificial intelligence,Machine learning | Conference |
Volume | ISSN | Citations |
6869 | 0302-9743 | 5 |
PageRank | References | Authors |
0.48 | 11 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Saguna | 1 | 51 | 5.28 |
| Arkady B. Zaslavsky | 2 | 943 | 168.27 |
| Dipanjan Chakraborty | 3 | 1761 | 118.69 |