Exploring Multimodal Biosignal Features For Stress Detection During Indoor Mobility

This paper presents a multimodal framework for assessing the emotional and cognitive experience of blind and visually impaired people when navigating in unfamiliar indoor environments based on mobile monitoring and fusion of electroencephalography (EEG) and electrodermal activity (EDA) signals. The overall goal is to understand which environmental factors increase stress and cognitive load in order to help design emotionally intelligent mobility technologies that are able to adapt to stressful environments from real-time biosensor data. We propose a model based on a random forest classifier which successfully infers in an automatic way (weighted AUROC 79.3%) the correct environment among five predefined categories expressing generic everyday situations of varying complexity and difficulty, where different levels of stress are likely to occur. Time-locating 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.