Abstract
Mobile eye-trackers are currently used during real-world tasks (e.g. gait) to monitor visual and cognitive processes, particularly in ageing and Parkinson’s disease (PD). However, contextual analysis involving fixation locations during such tasks is rarely performed due to its complexity. This study adapted a validated algorithm and developed a classification method to semi-automate contextual analysis of mobile eye-tracking data. We further assessed inter-rater reliability of the proposed classification method. A mobile eye-tracker recorded eye-movements during walking in five healthy older adult controls (HC) and five people with PD. Fixations were identified using a previously validated algorithm, which was adapted to provide still images of fixation locations (n = 116). The fixation location was manually identified by two raters (DH, JN), who classified the locations. Cohen’s kappa correlation coefficients determined the inter-rater reliability. The algorithm successfully provided still images for each fixation, allowing manual contextual analysis to be performed. The inter-rater reliability for classifying the fixation location was high for both PD (kappa = 0.80, 95% agreement) and HC groups (kappa = 0.80, 91% agreement), which indicated a reliable classification method. This study developed a reliable semi-automated contextual analysis method for gait studies in HC and PD. Future studies could adapt this methodology for various gait-related eye-tracking studies.
Highlights
Eye-tracking during real-world tasks is increasingly popular within various fields of research, including neurology [1], psychiatry [2] and human movement science [3]
The adapted mobile eye-tracker algorithm produced 116 still images of fixation locations from data obtained when walking in healthy older adult controls (HC) and Parkinson’s disease (PD) participants
Inter-rater reliability results for fixation location identification are displayed in Tables 2 and 3
Summary
Eye-tracking during real-world tasks is increasingly popular within various fields of research, including neurology [1], psychiatry [2] and human movement science [3]. Eyemovements can be broken into two classifications: saccadic fast eye-movements which shift foveation between different areas of interest within the environment, and fixation eyemovements (including smooth pursuits) where the eye pauses on areas of interest [4]. Increased popularity in recording eyemovements ( saccades) is due to their known relationships with cognitive and visual processes [5], allowing inferences regarding impairment of these underlying functions. Describing eye-movements during real-world tasks (i.e. walking, driving, obstacle crossing) is important to understand visuo-cognitive impairment and develop effective interventions in ageing and neurodegenerative disorders such as Parkinson’s disease (PD). Mobile infrared or video-based eye-trackers provide comprehensive recording of temporal and spatial features of eye-
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