Multimer: Modeling Neurophysiological Experience in Public Urban Space

Measuring and analyzing spatial, multimodal biosensor data may effectively model how the built environment influences neurophysiological processes. This article presents the Multimer Data Collection and Analysis System (MDCAS), which records data from several kinds of commonly available, wearable sensors (wearables) including electroencephalogram (EEG), electrocardiogram (ECG), pedometer, accelerometer, and gyroscope modules. Data from these wearables is sent to a custom smartphone application, which also records surveys and associates these with global positioning system (GPS) readings. MDCAS then collects and analyzes data from its smartphone app. MDCAS aims to help space professionals like architects, workplace strategists, and urban planners make better design interventions. As a case study of the MDCAS, this article discusses the analysis results of biometric data (EEG, ECG in addition to survey reports) collected from a 2017 study focused on pedestrians, cyclists, and drivers (N = 101) in New York City. Signal and spatial validation of the data indicated usability—data that is not randomly distributed—for biometric data types. Exploratory regressions of the biometric data (regressors) with exogenous data (predictors including environmental and municipal data sets) revealed spatiotemporal relationships that warrant further investigation. Notable relationships include 1) EEG beta and gamma frequencies were more strongly predicted by street features like service capacity (e.g. delivery levels) and speed limit, while EEG delta and theta frequencies were more strongly predicted by amenities like cultural institutions and trees; 2) pedestrians and cyclists were more impacted by street features during weekdays, and 3) a non-oppositional relationship between EEG beta/gamma and delta/theta frequencies. © 2020, Springer Nature Switzerland AG.