title: Quantifying saccades while walking: validity of a novel velocity-based algorithm for mobile eye tracking.
abstract: 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.
authors.forename: Samuel
authors.initials: S
authors.lastname: Stuart
authors.forename: Brook
authors.initials: B
authors.lastname: Galna
authors.forename: Sue
authors.initials: S
authors.lastname: Lord
authors.forename: Lynn
authors.initials: L
authors.lastname: Rochester
authors.forename: Alan
authors.initials: A
authors.lastname: Godfrey
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001767:Blinking; D016022:Case-Control Studies; D006801:Humans; D008875:Middle Aged; D010300:Parkinson Disease / Q000503:physiopathology; D012438:Saccades; D016138:Walking