Analysis of the Usefulness of Mobile Eyetracker for the Recognition of Physical Activities

Peter Hevesi, Jamie A. Ward, Orkhan Amiraslanov, Gerald Pirkl, Paul Lukowicz

In: The International Academy, Research and Industry Association. International Conference on Mobile Ubiquitous Computing, Systems, Services and Technologies (UBICOMM-2017) November 12-16 Barcelona Spain Pages 5-10 ISBN 978-1-61208-598-2 IARIA XPS Press 2017.


We investigate the usefulness of information from a wearable eyetracker to detect physical activities during assembly and construction tasks. Large physical activities, like carrying heavy items and walking, are analysed alongside more precise, hand-tool activities like using a screwdriver. Statistical analysis of eye based features like fixation length and frequency of fixations show significant correlations for precise activities. Using this finding, we selected 10, calibration-free eye features to train a classifier for recognising up to 6 different activities. Frame-by- frame and event based results are presented using data from an 8-person dataset containing over 600 activity events. We also evaluate the recognition performance when gaze features are combined with data from wearable accelerometers and microphones. Our initial results show a duration-weighted event precision and recall of up to 0.69 & 0.84 for independently trained recognition on precise activities using gaze. This indicates that gaze is suitable for spotting subtle precise activities and can be a useful source for more sophisticated classifier fusion.

2017_Ubicomm_Eyetracker.pdf (pdf, 6 MB)

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz