Reliable Trajectory Classification Using Wi-Fi Signal Strength in Indoor Scenarios

M. Werner, L. Schauer, A. Scharf

Abstract: The time-series nature of human movement inside buildings can be exploited for common tasks of location-based computing. With this paper, we propose to use Wi-Fi signal strength measurements directly to infer the trajectory in comparison with a database of trajectories removing the need for accurate map information or fingerprint databases. A trajectory consists of a time-series of sensor readings of all Wi-Fi signals in reach measured by a mobile device. Starting from these measurements, we discuss several possibilities of denoising, filtering and classification of trajectories to improve our approach. By using a variant of the Douglas-Peucker algorithm we reduce the amount of computation without severe degradation of classification performance. Furthermore, we increase platform scalability by using a fast filter operation based on the Jaccard index of presence of access points to prune irrelevant trajectories early. With respect to our setting, the Frechet-distance between trajectories has proven to be a very good choice outperforming dynamic time warping. Finally, we introduce several data-driven trajectory segmentation schemes in order to be able to match partial trajectories early. The evaluation is based on the collection of trajectories in specific situations including staircases, hallways and movement inside a single room. With this approach, we are able to reliable classify trajectories without an intermediate step of calculating spatial position. This results in increased stability with respect to local changes in the environment, as these changes only affect a small part of a longer trajectory.
Published in: Proceedings of IEEE/ION PLANS 2014
May 5 - 8, 2014
Hyatt Regency Hotel
Monterey, CA
Pages: 663 - 670
Cite this article: Werner, M., Schauer, L., Scharf, A., "Reliable Trajectory Classification Using Wi-Fi Signal Strength in Indoor Scenarios," Proceedings of IEEE/ION PLANS 2014, Monterey, CA, May 2014, pp. 663-670.
Full Paper: ION Members/Non-Members: 1 Download Credit
Sign In