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- W2775684712 abstract "We investigate the usefulness of information from awearable eyetracker to detect physical activities during assemblyand construction tasks. Large physical activities, like carryingheavy items and walking, are analysed alongside more precise,hand-tool activities like using a screwdriver. Statistical analysis ofeye based features like fixation length and frequency of fixationsshow significant correlations for precise activities. Using thisfinding, we selected 10, calibration-free eye features to train aclassifier for recognising up to 6 different activities. Frame-byframeand event based results are presented using data froman 8-person dataset containing over 600 activity events. Wealso evaluate the recognition performance when gaze featuresare combined with data from wearable accelerometers andmicrophones. Our initial results show a duration-weighted eventprecision and recall of up to 0.69 & 0.84 for independently trainedrecognition on precise activities using gaze. This indicates thatgaze is suitable for spotting subtle precise activities and can bea useful source for more sophisticated classifier fusion." @default.
- W2775684712 created "2017-12-22" @default.
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- W2775684712 date "2017-11-12" @default.
- W2775684712 modified "2023-09-26" @default.
- W2775684712 title "Analysis of the Usefulness of Mobile Eyetracker for the Recognition of Physical Activities" @default.
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