Using Bluetooth proximity sensing to determine where office workers spend time at work

Clark, Bronwyn K, Winkler, Elisabeth A, Brakenridge, Charlotte L, Trost, Stewart G and Healy, Genevieve N (2018) Using Bluetooth proximity sensing to determine where office workers spend time at work. PloS one, 13 3: e0193971. doi:10.1371/journal.pone.0193971

Author Clark, Bronwyn K
Winkler, Elisabeth A
Brakenridge, Charlotte L
Trost, Stewart G
Healy, Genevieve N
Title Using Bluetooth proximity sensing to determine where office workers spend time at work
Journal name PloS one   Check publisher's open access policy
ISSN 1932-6203
Publication date 2018-03-07
Sub-type Article (original research)
DOI 10.1371/journal.pone.0193971
Open Access Status DOI
Volume 13
Issue 3
Start page e0193971
Publisher Public Library of Science
Language eng
Subject 1300 Biochemistry, Genetics and Molecular Biology
1100 Agricultural and Biological Sciences
Abstract Most wearable devices that measure movement in workplaces cannot determine the context in which people spend time. This study examined the accuracy of Bluetooth sensing (10-second intervals) via the ActiGraph GT9X Link monitor to determine location in an office setting, using two simple, bespoke algorithms.

For one work day (mean±SD 6.2±1.1 hours), 30 office workers (30% men, aged 38±11 years) simultaneously wore chest-mounted cameras (video recording) and Bluetooth-enabled monitors (initialised as receivers) on the wrist and thigh. Additional monitors (initialised as beacons) were placed in the entry, kitchen, photocopy room, corridors, and the wearer's office. Firstly, participant presence/absence at each location was predicted from the presence/absence of signals at that location (ignoring all other signals). Secondly, using the information gathered at multiple locations simultaneously, a simple heuristic model was used to predict at which location the participant was present. The Bluetooth-determined location for each algorithm was tested against the camera in terms of F-scores.

When considering locations individually, the accuracy obtained was excellent in the office (F-score = 0.98 and 0.97 for thigh and wrist positions) but poor in other locations (F-score = 0.04 to 0.36), stemming primarily from a high false positive rate. The multi-location algorithm exhibited high accuracy for the office location (F-score = 0.97 for both wear positions). It also improved the F-scores obtained in the remaining locations, but not always to levels indicating good accuracy (e.g., F-score for photocopy room ≈0.1 in both wear positions).

The Bluetooth signalling function shows promise for determining where workers spend most of their time (i.e., their office). Placing beacons in multiple locations and using a rule-based decision model improved classification accuracy; however, for workplace locations visited infrequently or with considerable movement, accuracy was below desirable levels. Further development of algorithms is warranted.
Q-Index Code C1
Q-Index Status Provisional Code
Grant ID 1057608
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: Pubmed Import
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Created: Wed, 14 Mar 2018, 10:06:00 EST