Name
Abstract | ||
|---|---|---|
Multimodel wearable sensor data classificationplays an important role in ubiquitous computingand has a wide range of applications in variousscenarios from healthcare to entertainment. How-ever, most of the existing work in this field em-ploys domain-specific approaches and is thus inef-fective in complex situations where multi-modalitysensor data is collected. Moreover, the wearablesensor data is less informative than the conven-tional data such as texts or images. In this paper,to improve the adaptability of such classificationmethods across different application contexts, weturn this classification task into a game and applya deep reinforcement learning scheme to dynami-cally deal with complex situations. We also intro-duce a selective attention mechanism into the rein-forcement learning scheme to focus on the crucialdimensions of the data. This mechanism helps tocapture extra information from the signal, and canthus significantly improve the discriminative powerof the classifier. We carry out several experimentson three wearable sensor datasets, and demonstratecompetitive performance of the proposed approachcompared to several state-of-the-art baselines. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.24963/ijcai.2018/432 | IJCAI |
DocType | Volume | Citations |
Conference | abs/1804.05493 | 3 |
PageRank | References | Authors |
0.38 | 12 | 7 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiang Zhang | 1 | 41 | 8.15 |
| Lina Yao | 2 | 981 | 93.63 |
| Chaoran Huang | 3 | 34 | 6.64 |
| Sen Wang | 4 | 477 | 37.24 |
| Mingkui Tan | 5 | 501 | 38.31 |
| Guodong Long | 6 | 655 | 47.27 |
| Can Wang | 7 | 202 | 17.59 |