Designing for the investigation of microclimate stressors and physiological and neurological responses from the perspective of maker culture

Abstract

The 2021 United Nations Climate Change Conference (COP26) resulted in the Glasgow Climate Pact. Initial work in the study reported in this paper investigated relationships between environment and physiological measurements using smartwatches, and self-designed bespoke environmental modules which are wearable around the waist. Data from this initial phase was analysed with a Random Forest regression model. The next phase of this project involves neurophysiological measurement, specifically electroencephalography (EEG). EEG was introduced to the model to explore how the changes in environmental or biometric measurements correlate with changes in neurophysiological measurements. In this latter phase, EEG data is viewed as an independent data type that is distinct from environmental and other physiological data. The headset model used to record EEG data is again a bespoke hand-made design, comprising a combination of biosensing board and electrodes from OpenBCI and widely available items like adhesive tapes and staples. A subsequent step involved validation of this DIY EEG headset data against research grade equipment, of which the analysis of different features of EEG data have shown to be of statistically comparable trends. For data collection, all data recorded is stored in Google Drive; Python is used to synchronize, pre-process data and train regression models. The first headset prototype was assembled in mid-October 2021, and was tested and developed in early November. From mid-November to late January 2022, the authors wore the devices for one to two hours per day to collect data. For EEG data, eight channels were recorded, basic filters (bandpass and notch) and REST re-referencing are applied. In this project, EEG time-series are used as input in regression models with other data types as output. Two regression models were trained then compared, the first being convolutional neural network with pre-built architecture and the other being a Random Forest model with features extracted from EEG time-series. Inferences are made from the models using open-source interpreters, with an eventual aim to infer how one's local environment might impact one's emotions and health. The results suggest that sound level, carbon dioxide concentration, and dust concentration feature more importantly in the regression models trained on collected datasets. These factors were continually associated with high feature importance scores in the EEG data signal and in both the objective scores recorded from the electronic instruments and the more subjective self-report forms. Furthermore, it was found that visual stimulus and problem processing, in terms of information, touch, and spatial relationships, are the most influential factors affecting the participants' physiological well-being in this research. Most recently, one aspect that is currently being investigated is electrodermal activity (EDA). EDA is marker of sympathetic network activity (Zangróniz et al., 2017). As such, it is an indication of human stress and emotion arousal, (Rahma et al., 2022). It is hoped that analyses of EDA data will further strengthen the emerging model describing the intersections between local microclimate and physiological and neurological stress. Early validation experiments comparing DIY EDA devices against research-grade Empactica E4 sets have shown promising results.

Keywords: microclimate, maker culture, wearable sensors, IoT

DOI: 10.54941/ahfe1004042