Name
Abstract | ||
|---|---|---|
Context-aware computing is increasingly paid much attention, especially makes the people's social contextual behavior very crucial for user-centric dynamic behavior inference. At present, extensive work has focused on detecting specific places inferred by static radio signals like GPS, GSM and WiFi, and recognizing mobility modes inferred by embedded sensor components like accelerometer. This paper proposes a distinct feature based classification approach and context restraint based majority vote rule to infer social contextual behavior in dynamic surroundings. Experimental results indicate that our proposed method can achieve high accuracy for inferring social contextual behavior through the real-life Bluetooth traces. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2494091.2494176 | UbiComp (Adjunct Publication) |
Keywords | Field | DocType |
extensive work,embedded sensor component,context-aware computing,context restraint,social contextual behavior,user-centric dynamic behavior inference,distinct feature,dynamic surrounding,classification approach,bluetooth trace,bluetooth | GSM,Computer science,Accelerometer,Inference,Human–computer interaction,Global Positioning System,Feature based,Majority rule,Bluetooth | Conference |
Citations | PageRank | References |
8 | 0.62 | 4 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhenyu Chen | 1 | 470 | 25.35 |
| Yiqiang Chen | 2 | 1446 | 109.32 |
| Shuangquan Wang | 3 | 272 | 22.46 |
| Junfa Liu | 4 | 357 | 26.85 |
| Xingyu Gao | 5 | 106 | 14.95 |
| Andrew T. Campbell | 6 | 8958 | 759.66 |