Identifying Urban Mobility Challenges for the Visually Impaired with Mobile Monitoring of Multimodal Biosignals

In this study, we aim to better the user experience of the visually impaired when navigating in unfamiliar outdoor environments assisted by mobility technologies. We propose a framework for assessing their cognitive-emotional experience based on ambulatory monitoring and multimodal fusion of electroencephalography, electrodermal activity, and blood volume pulse signals. The proposed model is based on a random forest classifier which successfully infers in an automatic way the correct urban environment among eight predefined categories (AUROC 93 %). Geolocating the most predictive multimodal features that relate to cognitive load and stress, we provide further insights into the relationship of specific biomarkers with the environmental/situational factors that evoked them.