  In this study, we aim to better understand the cognitive-emotional experience
of visually impaired people when navigating in unfamiliar urban environments,
both outdoor and indoor. We propose a multimodal framework based on random
forest classifiers, which predict the actual environment among predefined
generic classes of urban settings, inferring on real-time, non-invasive,
ambulatory monitoring of brain and peripheral biosignals. Model performance
reached 93% for the outdoor and 87% for the indoor environments (expressed in
weighted AUROC), demonstrating the potential of the approach. Estimating the
density distributions of the most predictive biomarkers, we present a series of
geographic and temporal visualizations depicting the environmental contexts in
which the most intense affective and cognitive reactions take place. A linear
mixed model analysis revealed significant differences between categories of
vision impairment, but not between normal and impaired vision. Despite the
limited size of our cohort, these findings pave the way to emotionally
intelligent mobility-enhancing systems, capable of implicit adaptation not only
to changing environments but also to shifts in the affective state of the user
in relation to different environmental and situational factors.
