2 research outputs found
Analysis of the Usefulness of Mobile Eyetracker for the Recognition of Physical Activities
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-byframe
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