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

Authors:
Peter Hevesi
Jamie Ward
Orkhan Amiraslanov
Gerald Pirkl
Paul Lukowicz

Keywords: eyetracker; activity recognition; sensor fusion

Abstract:
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.

Pages: 5 to 10

Copyright: Copyright (c) IARIA, 2017

Publication date: November 12, 2017

Published in: conference

ISSN: 2308-4278

ISBN: 978-1-61208-598-2

Location: Barcelona, Spain

Dates: from November 12, 2017 to November 16, 2017