Group affiliation detection in a challenging environment

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


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.


Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Computer Science
Original languageEnglish
Title of host publicationThe 5th International Symposium on Emerging Information, Communication and Networks
PublisherElsevier Limited
Publication statusPublished - 2018
Publication categoryResearch

Related research output

Jonsson, H., 2018 Jun 15, Lund: Computer Science, Lund University. 140 p.

Research output: ThesisDoctoral Thesis (compilation)

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