We validate a novel algorithm to detect saccades from raw data obtained during walking from a mobile infra-red eye-tracking device. The algorithm was based on a velocity threshold detection method, which excluded artefacts such as blinks and flickers using specific criteria. Mobile infra-red eye-tracking was performed with a group of healthy older adults (n=5) and Parkinson's disease (n=5) subjects. Saccades determined from raw eye tracker data obtained during walking using the algorithm were compared to a ground truth dataset defined as frame-by-frame visual inspection of raw eye-tracking videos. 100 trials from 10 subjects were analyzed and compared. The algorithm was highly reliable when compared to the ground truth (ICC(2,1) = 0.94), with an overall correct saccade detection percentage of 85%. This provides a simple yet robust algorithm for the analysis of mobile eye-tracking data.
|Title of host publication||2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014|
|Number of pages||4|
|Publication status||Published - 6 Nov 2014|
|Event||2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014 - Chicago, United States|
Duration: 26 Aug 2014 → 30 Aug 2014
|Conference||2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2014|
|Period||26/08/14 → 30/08/14|