Finding f-formations using dominant sets in the Bluetooth proximity data of the Conflab data set

Bachelor thesis (2022)

Authors

B. Dikker Electrical Engineering, Mathematics and Computer Science

Contributors

S. Tan Pattern Recognition and Bioinformatics - EEMCS (mentor)
H.S. Hung Pattern Recognition and Bioinformatics - EEMCS (mentor)
O.E. Scharenborg (graduation committee member)
Faculty
Electrical Engineering, Mathematics and Computer Science, Electrical Engineering, Mathematics and Computer Science
Copyright: © 2022 Bram Dikker
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Published Date

28-01-2022

Language

English

Reuse Rights

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

When analysing social interactions, manual labour is often required to identify what is happening. An automated method of detecting who is interacting with who would already prove to be a significant help. This paper looks at how automated interaction detecting can be established. We look at methods of detecting proximity and look deeper into detecting F-formations using proximity. An f-formation is a group of people who are standing together with the intention of conversing. We show that it is possible to detect f-formations using a data-set containing proximity information and f-formations as ground truths. Our results show that using only proximity data from this dataset; we can detect f-formations better than the baseline provided in that dataset.

Files

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