Name
Abstract | ||
|---|---|---|
A major challenge in human activity recognition over long periods with multiple sensors is clock synchronization of independent data streams. Poor clock synchronization can lead to poor data and classifiers. In this paper, we propose a hybrid synchronization approach that combines NTP (Network Time Protocol) and context markers. Our evaluation shows that our approach significantly reduces synchronization error (20 ms) when compared to approaches that rely solely on NTP or sensor events. Our proposed approach can be applied to any wearable sensor where an independent sensor stream requires synchronization.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3341162.3349334 | Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers |
Keywords | Field | DocType |
clock drifts, clock synchronization, wearable sensors | Data quality,Computer science,Wearable computer,Clock synchronization,Computer hardware,Embedded system | Conference |
ISBN | Citations | PageRank |
978-4503-6869-8 | 1 | 0.35 |
References | Authors | |
2 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chaofan Wang | 1 | 5 | 4.66 |
| Zhanna Sarsenbayeva | 2 | 50 | 10.72 |
| Chu Luo | 3 | 84 | 12.18 |
| Goncalves, J. | 4 | 404 | 42.24 |
| Vassilis Kostakos | 5 | 1718 | 138.50 |