Designing for the investigation of microclimate stressors and
physiological and neurological responses from the perspective of maker
culture
Authors: Tuan Nguyen Thien Minh , Anh Nguyen Duc Minh , Y T Lim Kenneth 
Published: July 2023

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.