Compensation of Head Movements in Mobile Eye-Tracking Data Using an Inertial Measurement Unit
Författare
Redaktör
- AJ Brush
- Adrian Friday
- Julie Kientz
- Junehwa Song
Summary, in English
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.
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.
Avdelning/ar
Publiceringsår
2014
Språk
Engelska
Sidor
1161-1167
Publikation/Tidskrift/Serie
Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing: Adjunct Publication
Dokumenttyp
Konferensbidrag
Förlag
Association for Computing Machinery (ACM)
Ämne
- Medical Engineering
Status
Published
ISBN/ISSN/Övrigt
- ISBN: 978-1-4503-3047-3