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GlassesValidator : A data quality tool for eye tracking glasses

Niehorster, Diederick C LU orcid ; Hessels, Roy S ; Benjamins, Jeroen S ; Nyström, Marcus LU orcid and Hooge, Ignace T C LU (2023) In Behavior Research Methods
Abstract

According to the proposal for a minimum reporting guideline for an eye tracking study by Holmqvist et al. (2022), the accuracy (in degrees) of eye tracking data should be reported. Currently, there is no easy way to determine accuracy for wearable eye tracking recordings. To enable determining the accuracy quickly and easily, we have produced a simple validation procedure using a printable poster and accompanying Python software. We tested the poster and procedure with 61 participants using one wearable eye tracker. In addition, the software was tested with six different wearable eye trackers. We found that the validation procedure can be administered within a minute per participant and provides measures of accuracy and precision.... (More)

According to the proposal for a minimum reporting guideline for an eye tracking study by Holmqvist et al. (2022), the accuracy (in degrees) of eye tracking data should be reported. Currently, there is no easy way to determine accuracy for wearable eye tracking recordings. To enable determining the accuracy quickly and easily, we have produced a simple validation procedure using a printable poster and accompanying Python software. We tested the poster and procedure with 61 participants using one wearable eye tracker. In addition, the software was tested with six different wearable eye trackers. We found that the validation procedure can be administered within a minute per participant and provides measures of accuracy and precision. Calculating the eye-tracking data quality measures can be done offline on a simple computer and requires no advanced computer skills.

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
epub
subject
keywords
Eye tracking, Data quality, Calibration, Validation, Accuracy, Reporting practices
in
Behavior Research Methods
pages
9 pages
publisher
Springer
external identifiers
  • scopus:85161964421
  • pmid:37326770
ISSN
1554-3528
DOI
10.3758/s13428-023-02105-5
language
English
LU publication?
yes
additional info
© 2023. The Author(s).
id
05f60df8-d808-4c82-9f6c-86f28c007fba
date added to LUP
2023-06-23 07:38:56
date last changed
2024-04-19 23:05:12
@article{05f60df8-d808-4c82-9f6c-86f28c007fba,
  abstract     = {{<p>According to the proposal for a minimum reporting guideline for an eye tracking study by Holmqvist et al. (2022), the accuracy (in degrees) of eye tracking data should be reported. Currently, there is no easy way to determine accuracy for wearable eye tracking recordings. To enable determining the accuracy quickly and easily, we have produced a simple validation procedure using a printable poster and accompanying Python software. We tested the poster and procedure with 61 participants using one wearable eye tracker. In addition, the software was tested with six different wearable eye trackers. We found that the validation procedure can be administered within a minute per participant and provides measures of accuracy and precision. Calculating the eye-tracking data quality measures can be done offline on a simple computer and requires no advanced computer skills.</p>}},
  author       = {{Niehorster, Diederick C and Hessels, Roy S and Benjamins, Jeroen S and Nyström, Marcus and Hooge, Ignace T C}},
  issn         = {{1554-3528}},
  keywords     = {{Eye tracking; Data quality; Calibration; Validation; Accuracy; Reporting practices}},
  language     = {{eng}},
  month        = {{06}},
  publisher    = {{Springer}},
  series       = {{Behavior Research Methods}},
  title        = {{GlassesValidator : A data quality tool for eye tracking glasses}},
  url          = {{http://dx.doi.org/10.3758/s13428-023-02105-5}},
  doi          = {{10.3758/s13428-023-02105-5}},
  year         = {{2023}},
}