@inproceedings{10.1145/3600100.3626263,
title = {A Trajectory Estimation Method from Spatially Sparse and Noisy Beacon Data Based on Spring Dynamics},
author = {Junya Maruyama and Yudai Honma and Yuuki Nishiyama and Yoshihiro Kawahara},
url = {https://doi.org/10.1145/3600100.3626263
https://buildsys.acm.org/2023/},
doi = {10.1145/3600100.3626263},
isbn = {9798400702303},
year = {2023},
date = {2023-11-15},
urldate = {2023-11-15},
booktitle = {Proceedings of the 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation},
pages = {286–287},
publisher = {Association for Computing Machinery},
address = {Istanbul, Turkey},
series = {BuildSys '23},
abstract = {Analysis of trajectory data within buildings offers insights for optimizing environmental design and habitability. However, data from indoor location sensors tend to be sparse and noisy. This makes it difficult for conventional route estimation models to be applied effectively. Our study seeks to derive detailed, temporally, and spatially rich trajectory data from this compromised sensor information. We achieve this by interpreting trajectories as continuous stay points. To facilitate this, we introduce a building corridor network that conceptualizes buildings as a series of points. Routes are inferred using a sequence estimation model applied to this network. This approach employs spring dynamics, which balance the resistance to staying with the attraction to specific beacons, via mathematical optimization. Notably, our model can deduce a trajectory of 131 points from only 15 beacons with, an accuracy rate of 87. Our method presents a promising avenue for capturing extensive route data.},
keywords = {Architectural Space, Building corridor Network, Human trajectory},
pubstate = {published},
tppubtype = {inproceedings}
}