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.

中文翻译：

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使用脑电图和外周生物信号对视力障碍者的压力环境进行多模态分类

在这项研究中，我们旨在更好地了解视障人士在室外和室内陌生的城市环境中导航时的认知-情感体验。我们提出了一个基于随机森林分类器的多模态框架，它预测预定义的城市环境通用类别中的实际环境，推断实时、非侵入性、动态监测大脑和外围生物信号。模型性能在室外环境中达到 93%，在室内环境中达到 87%（以加权 AUROC 表示），证明了该方法的潜力。估计最具预测性的生物标志物的密度分布，我们展示了一系列地理和时间可视化，描绘了最强烈的情感和认知反应发生的环境背景。线性混合模型分析揭示了视力障碍类别之间的显着差异，但正常视力和视力受损之间没有显着差异。尽管我们的队列规模有限，但这些发现为情绪智能移动增强系统铺平了道路，该系统不仅能够隐式适应不断变化的环境，而且能够适应用户的情感状态与不同环境和情境因素相关的变化。