Name
Abstract | ||
|---|---|---|
This paper explores the feasibility of automatically discriminating users from the activity as well as temporal information of their daily routine. We observe that everyone pursues a daily semi-regular activity pattern. Based on this observation, we have developed a system UDAT and experimented on Microsoft Geolife as well as UDAT datasets. With Geolife transportation activity log and UDAT motion-static activity log, the system achieves 73.3% and 80.68% accuracy, respectively. Although the overall system accuracy is moderate, the system achieves the highest accuracy when the users belong to the different activity buckets. This signifies the utility of two-phase classification for user discrimination. |
Year | DOI | Venue |
|---|---|---|
2017 | 10.1109/MDM.2017.59 | 2017 18th IEEE International Conference on Mobile Data Management (MDM) |
Keywords | Field | DocType |
user identification,activity based classification | Data mining,Data modeling,Activity recognition,Computer science,Accelerometer | Conference |
ISSN | ISBN | Citations |
1551-6245 | 978-1-5386-3933-7 | 0 |
PageRank | References | Authors |
0.34 | 10 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Snigdha Das | 1 | 43 | 4.08 |
| Dibya Jyoti Roy | 2 | 0 | 0.34 |
| Subrata Nandi | 3 | 71 | 21.37 |
| Sandip Chakraborty | 4 | 24 | 16.46 |
| Bivas Mitra | 5 | 98 | 25.41 |