Name
Abstract | ||
|---|---|---|
The automatic recognition of child activity using multi-sensor data enables various applications such as child-development monitoring, energy-expenditure estimation, child-obesity prevention, child safety in and around the home, etc. We formulate the activity recognition task as a classification problem based on multiple sensors embedded in a wearable device. The approach we propose in this paper isto apply spectral analysis techniques of multiple sensor data for activity recognition. Quadratic Discriminant Analysis (QDA) classifieris then trained using manually annotated data and applied for activity recognition. The obtained experimental results for the recognition of 7 activities based on a limited data set are promising and show the potential of the proposed method. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1931344.1931382 | MB |
Field | DocType | Citations |
Activity classification,Activity recognition,Pattern recognition,Wearable computer,Feature extraction,Speech recognition,Feature (machine learning),Artificial intelligence,Spectral analysis,Engineering,Multiple sensors,Quadratic classifier | Conference | 4 |
PageRank | References | Authors |
0.46 | 6 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Sabri Boughorbel | 1 | 127 | 15.32 |
| Jeroen Breebaart | 2 | 293 | 28.86 |
| Fons Bruekers | 3 | 97 | 10.95 |
| Ingrid Flinsenberg | 4 | 5 | 0.82 |
| Warner ten Kate | 5 | 176 | 10.47 |