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Compensation of Head Movements in Mobile Eye-Tracking Data Using an Inertial Measurement Unit

Larsson, Linnéa LU ; Schwaller, Andrea; Holmqvist, Kenneth LU ; Nyström, Marcus LU and Stridh, Martin LU (2014) In Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication p.1161-1167
Abstract
Analysis of eye movements recorded with a mobile eye-tracker is difficult since the eye-tracking data are severely affected by simultaneous head and body movements. Automatic analysis methods developed for remote-, and tower-mounted eye-trackers do not take this into account and are therefore not suitable to use for data where also head- and body movements are present. As a result, data recorded with a mobile eye-tracker are often analyzed manually.

In this work, we investigate how simultaneous recordings of eye- and head movements can be employed to isolate the motion of the eye in the eye-tracking data. We recorded eye-in-head movements with a mobile eye-tracker and head movements with an Inertial Measurement Unit (IMU).... (More)
Analysis of eye movements recorded with a mobile eye-tracker is difficult since the eye-tracking data are severely affected by simultaneous head and body movements. Automatic analysis methods developed for remote-, and tower-mounted eye-trackers do not take this into account and are therefore not suitable to use for data where also head- and body movements are present. As a result, data recorded with a mobile eye-tracker are often analyzed manually.

In this work, we investigate how simultaneous recordings of eye- and head movements can be employed to isolate the motion of the eye in the eye-tracking data. We recorded eye-in-head movements with a mobile eye-tracker and head movements with an Inertial Measurement Unit (IMU). Preliminary results show that by compensating the eye-tracking data with the estimated head orientation, the standard deviation of the data during vestibular-ocular reflex (VOR) eye movements, was reduced from 8.0 to 0.9 in the vertical direction and from 12.9 to 0.6 in the horizontal direction. This suggests that a head compensation algorithm based on IMU data can be used to isolate the movements of the eye and therefore simplify the analysis of data recorded using a mobile eye-tracker. (Less)
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author
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
in
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication
editor
Brush, AJ; Friday, Adrian; Kientz, Julie; Song, Junehwa; ; ; and
pages
1161 - 1167
publisher
ACM
external identifiers
  • scopus:84908667525
ISBN
978-1-4503-3047-3
DOI
10.1145/2638728.2641693
language
English
LU publication?
yes
id
5ae18e1c-c5b7-41ae-96c8-1a947620f629 (old id 4693713)
date added to LUP
2014-10-17 12:38:01
date last changed
2017-04-16 04:28:29
@inproceedings{5ae18e1c-c5b7-41ae-96c8-1a947620f629,
  abstract     = {Analysis of eye movements recorded with a mobile eye-tracker is difficult since the eye-tracking data are severely affected by simultaneous head and body movements. Automatic analysis methods developed for remote-, and tower-mounted eye-trackers do not take this into account and are therefore not suitable to use for data where also head- and body movements are present. As a result, data recorded with a mobile eye-tracker are often analyzed manually. <br/><br>
In this work, we investigate how simultaneous recordings of eye- and head movements can be employed to isolate the motion of the eye in the eye-tracking data. We recorded eye-in-head movements with a mobile eye-tracker and head movements with an Inertial Measurement Unit (IMU). Preliminary results show that by compensating the eye-tracking data with the estimated head orientation, the standard deviation of the data during vestibular-ocular reflex (VOR) eye movements, was reduced from 8.0 to 0.9 in the vertical direction and from 12.9 to 0.6 in the horizontal direction. This suggests that a head compensation algorithm based on IMU data can be used to isolate the movements of the eye and therefore simplify the analysis of data recorded using a mobile eye-tracker.},
  author       = {Larsson, Linnéa and Schwaller, Andrea and Holmqvist, Kenneth and Nyström, Marcus and Stridh, Martin},
  booktitle    = {Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication},
  editor       = {Brush, AJ and Friday, Adrian and Kientz, Julie and Song, Junehwa},
  isbn         = {978-1-4503-3047-3},
  language     = {eng},
  pages        = {1161--1167},
  publisher    = {ACM},
  title        = {Compensation of Head Movements in Mobile Eye-Tracking Data Using an Inertial Measurement Unit},
  url          = {http://dx.doi.org/10.1145/2638728.2641693},
  year         = {2014},
}