Group affiliation detection in a challenging environment

Forskningsoutput: Kapitel i bok/rapport/Conference proceedingKonferenspaper i proceeding

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.

Detaljer

Författare
Enheter & grupper
Forskningsområden

Ämnesklassifikation (UKÄ) – OBLIGATORISK

  • Datavetenskap (datalogi)
Originalspråkengelska
Titel på värdpublikationThe 5th International Symposium on Emerging Information, Communication and Networks
FörlagElsevier Limited
Sidor507-512
Volym141
StatusPublished - 2018
PublikationskategoriForskning
Peer review utfördJa

Relaterad forskningsoutput

Jonsson, H., 2018 jun 15, Lund: Computer Science, Lund University. 140 s.

Forskningsoutput: AvhandlingDoktorsavhandling (sammanläggning)

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