Research Study Abstract

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

  • Published on Mar 7, 2018

Background: 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.

Methods: 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 (initialized as receivers) on the wrist and thigh. Additional monitors (initialized 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.

Results: 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).

Conclusions The Bluetooth signaling 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.


  • Bronwyn K. Clark 1
  • Elisabeth A. Winkler 1
  • Charlotte L. Brakenridge 1
  • Stewart G. Trost 2
  • Genevieve N. Healy 1


  • 1

    The University of Queensland, School of Public Health, Herston, Queensland, Australia

  • 2

    Institute of Health and Biomedical Innovation at Queensland, Centre for Children’s Health Research, Queensland University of Technology, South Brisbane, Queensland, Australia


Plos One


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