Improving wearable sensor data quality using context markers
Chaofan Wang, Zhanna Sarsenbayeva, Chu Luo, Jorge Goncalves, Vassilis Kostakos
Proceedings of the 2019 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2019 ACM International Symposium on Wearable Computers - UbiComp/ISWC '19 | ACM Press | Published : 2019
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.
Chaofan Wang is supported by a PhD scholarship provided by the Australian Commonwealth Government Research Training Program.