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Do you see what I see? Mobile eye-tracker contextual analysis and inter-rater reliability
1Institute of Neuroscience/Newcastle University Institute for Ageing, Clinical Ageing Research Unit, Newcastle University, Newcastle upon Tyne, NE4 5PL, UK. sam.stuart@newcastle.ac.uk.
Medical & Biological Engineering & Computing
|July 17, 2017
Summary
This study developed a reliable method to analyze eye movements during walking for people with Parkinson's disease (PD) and healthy older adults. The new technique simplifies contextual analysis of fixation locations, improving research in aging and PD gait.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Gerontology
Background:
- Mobile eye-trackers are crucial for studying visual and cognitive processes in real-world tasks, especially in aging populations and individuals with Parkinson's disease (PD).
- Complex contextual analysis of fixation locations during gait tasks using mobile eye-tracking data is rarely performed, limiting research insights.
Purpose of the Study:
- To adapt a validated algorithm and develop a semi-automated classification method for contextual analysis of mobile eye-tracking data during gait.
- To assess the inter-rater reliability of the proposed classification method for fixation locations in healthy controls (HC) and PD groups.
Main Methods:
- A mobile eye-tracker recorded eye movements during walking in five HC and five individuals with PD.
- A validated algorithm was adapted to generate still images of fixation locations (n=116) for manual classification by two independent raters.
- Inter-rater reliability was determined using Cohen's kappa correlation coefficients.
Main Results:
- The adapted algorithm successfully generated still images for each fixation, enabling manual contextual analysis.
- High inter-rater reliability was achieved for classifying fixation locations in both PD (kappa=0.80, 95% agreement) and HC (kappa=0.80, 91% agreement) groups.
- The developed method proved to be a reliable approach for contextual analysis in gait studies.
Conclusions:
- A reliable semi-automated method for contextual analysis of mobile eye-tracking data during gait has been developed and validated.
- This methodology shows promise for enhancing research in aging and Parkinson's disease gait studies.
- The approach can be adapted for diverse eye-tracking studies involving gait analysis.
