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

Overview of attention for article published in PLoS ONE, March 2018
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (60th percentile)
  • Above-average Attention Score compared to outputs of the same age and source (53rd percentile)

Mentioned by

twitter
4 tweeters

Citations

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2 Dimensions

Readers on

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21 Mendeley
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Title
Using Bluetooth proximity sensing to determine where office workers spend time at work
Published in
PLoS ONE, March 2018
DOI 10.1371/journal.pone.0193971
Pubmed ID
Authors

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

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.

Twitter Demographics

The data shown below were collected from the profiles of 4 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 21 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 21 100%

Demographic breakdown

Readers by professional status Count As %
Unspecified 4 19%
Other 4 19%
Student > Master 3 14%
Student > Bachelor 3 14%
Professor > Associate Professor 3 14%
Other 4 19%
Readers by discipline Count As %
Medicine and Dentistry 6 29%
Unspecified 5 24%
Computer Science 3 14%
Nursing and Health Professions 3 14%
Sports and Recreations 1 5%
Other 3 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 3. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 13 July 2018.
All research outputs
#6,313,637
of 12,342,061 outputs
Outputs from PLoS ONE
#54,640
of 135,383 outputs
Outputs of similar age
#108,660
of 275,979 outputs
Outputs of similar age from PLoS ONE
#1,248
of 2,711 outputs
Altmetric has tracked 12,342,061 research outputs across all sources so far. This one is in the 48th percentile – i.e., 48% of other outputs scored the same or lower than it.
So far Altmetric has tracked 135,383 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.7. This one has gotten more attention than average, scoring higher than 58% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 275,979 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 60% of its contemporaries.
We're also able to compare this research output to 2,711 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 53% of its contemporaries.