Name
Abstract | ||
|---|---|---|
In this paper, we study the problem of children activity recognition using smartwatch devices. We introduce the need for a robust children activity model and challenges involved. To address the problem, we employ two deep neural network models, specifically, Bi-Directional LSTM model and a fully connected deep network and compare the results to commonly used models in the area. We demonstrate that our proposed deep models can significantly improve results compared to baseline models. We further show benefits of activity intensity level detection in health monitoring and verify high performance of our proposed models in this task. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/EMBC.2018.8513320 | EMBC |
Field | DocType | Volume |
Computer vision,Data modeling,Activity recognition,Computer science,Recurrent neural network,Feature extraction,Activity intensity,Artificial intelligence,Artificial neural network,Smartwatch,Machine learning | Conference | 2018 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
7 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Anahita Hosseini | 1 | 24 | 3.66 |
| Shayan Fazeli | 2 | 0 | 0.68 |
| Eleanne van Vliet | 3 | 0 | 0.34 |
| Lisa Valencia | 4 | 0 | 0.34 |
| Rima Habre | 5 | 3 | 1.17 |
| Majid Sarrafzadeh | 6 | 3103 | 317.63 |
| Alex Bui | 7 | 318 | 48.20 |