Name
Abstract | ||
|---|---|---|
In this paper, we propose a human daily activity recognition method that is used for Ambient Assisted Living. The proposed system is able to learn a user's activities using the data from motion and door sensors. We extract low level features from the sensor data and feed the features to a model that combines support vector machines (SVMs) and conditional random fields (CRFs) to give accurate recognition results. We propose to combine SVM and CRF classifiers in a hierarchical model which results in better accuracies and can also make use of high level features. We conducted experiments and presented the effectiveness and accuracies of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/IECON.2012.6389449 | Montreal, QC |
Keywords | Field | DocType |
assisted living,feature extraction,pattern classification,support vector machines,ambient assisted living,crf classifiers,svm classifiers,conditional random fields,door sensors,hierarchical model,human daily activity recognition method,low level feature extraction,motion sensors | Conditional random field,Activity recognition,Pattern recognition,Random subspace method,Support vector machine,Feature extraction,Artificial intelligence,Engineering,Hierarchical database model,CRFS,Machine learning | Conference |
ISSN | ISBN | Citations |
1553-572X E-ISBN : 978-1-4673-2420-5 | 978-1-4673-2420-5 | 1 |
PageRank | References | Authors |
0.36 | 11 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Tirkaz, C. | 1 | 1 | 0.70 |
| Dietmar Bruckner | 2 | 17 | 4.17 |
| GuoQing Yin | 3 | 3 | 0.76 |
| Jan Haase | 4 | 65 | 8.01 |