Group affiliation detection in a challenging environment
(2018) 141. p.507-512- 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... (More) - 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/71597384-67f1-4e9c-a7e9-4f44ef37031d
- author
- Jonsson, Håkan LU and Nugues, Pierre LU
- organization
- publishing date
- 2018
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- The 5th International Symposium on Emerging Information, Communication and Networks
- volume
- 141
- pages
- 507 - 512
- publisher
- Elsevier
- external identifiers
-
- scopus:85058321043
- DOI
- 10.1016/j.procs.2018.10.134
- project
- Embedded Applications Software Engineering
- language
- English
- LU publication?
- yes
- id
- 71597384-67f1-4e9c-a7e9-4f44ef37031d
- date added to LUP
- 2018-09-13 15:35:07
- date last changed
- 2022-03-25 03:59:05
@inproceedings{71597384-67f1-4e9c-a7e9-4f44ef37031d, 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.<br/>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 = {{Jonsson, Håkan and Nugues, Pierre}}, booktitle = {{The 5th International Symposium on Emerging Information, Communication and Networks}}, language = {{eng}}, pages = {{507--512}}, publisher = {{Elsevier}}, title = {{Group affiliation detection in a challenging environment}}, url = {{http://dx.doi.org/10.1016/j.procs.2018.10.134}}, doi = {{10.1016/j.procs.2018.10.134}}, volume = {{141}}, year = {{2018}}, }