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


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.   [Download]


@inproceedings {Hevesi:Analysis:2017:9371,
	number = {}, 
	month = {}, 
	year = {2017}, 
	title = {Analysis of the Usefulness of Mobile Eyetracker for the Recognition of Physical Activities}, 
	journal = {}, 
	volume = {}, 
	pages = {5-10}, 
	publisher = {IARIA XPS Press}, 
	author = {Peter Hevesi, Jamie A. Ward, Orkhan Amiraslanov, Gerald Pirkl, Paul Lukowicz}, 
	keywords = {eyetracker; activity recognition; sensor fusion}