Algorithmic Derivation of Human Spatial Navigation Indices From Eye Movement Data
This work enables more cognitive assessments and early detection of spatial navigation impairments, particularly for individuals at risk of cognitive decline, but it is incremental as it applies existing methods to new data.
This study tackled the problem of assessing human spatial navigation by developing algorithmic indices from eye movement data, achieving notable accuracy with an R2 score of 0.72 for navigation and orientation and 0.50 for landmark recognition.
Spatial navigation is a complex cognitive function involving sensory inputs, such as visual, auditory, and proprioceptive information, to understand and move within space. This ability allows humans to create mental maps, navigate through environments, and process directional cues, crucial for exploring new places and finding one's way in unfamiliar surroundings. This study takes an algorithmic approach to extract indices relevant to human spatial navigation using eye movement data. Leveraging electrooculography signals, we analyzed statistical features and applied feature engineering techniques to study eye movements during navigation tasks. The proposed work combines signal processing and machine learning approaches to develop indices for navigation and orientation, spatial anxiety, landmark recognition, path survey, and path route. The analysis yielded five subscore indices with notable accuracy. Among these, the navigation and orientation subscore achieved an R2 score of 0.72, while the landmark recognition subscore attained an R2 score of 0.50. Additionally, statistical features highly correlated with eye movement metrics, including blinks, saccades, and fixations, were identified. The findings of this study can lead to more cognitive assessments and enable early detection of spatial navigation impairments, particularly among individuals at risk of cognitive decline.