Group affiliation detection in a challenging environment

Research output: Chapter in Book/Report/Conference proceedingPaper in conference proceeding

Bibtex

@inproceedings{7159738467f14e9ca7e94f44ef37031d,
title = "Group affiliation detection in a challenging environment",
abstract = "Social interaction sensing and indoor positioning using are widely researched. However, many use cases only need to determine proximity, and not the exact location. In this paper, we describe two methods to determine which meeting each user is participating in using proximity data collected from a challenging real-world office.We show that the RSSI threshold approach to detecting proximity is not feasible due to the optimal RSSI range being very small. Instead, we achieve an F-score of 82{\%} with a simple method, k-nearest neighbor classification, using data from the whole population. This method does not need any historical data or training, calibration to an environment, nor find a specific RSSI threshold. Finally, we present result from a user study with a prototype meeting application that identifies meeting participants, and advice on consequences of the above result for UI design.",
author = "H{\aa}kan Jonsson and Pierre Nugues",
year = "2018",
doi = "10.1016/j.procs.2018.10.134",
language = "English",
volume = "141",
pages = "507--512",
booktitle = "The 5th International Symposium on Emerging Information, Communication and Networks",
publisher = "Elsevier Limited",
address = "United Kingdom",

}