  Identifying stress levels can provide valuable data for mental health
analytics as well as labels for annotation systems. Although much research has
been conducted into stress detection models using heart rate variability at a
higher cost of data collection, there is a lack of research on the potential of
using low-resolution Electrodermal Activity (EDA) signals from consumer-grade
wearable devices to identify stress patterns. In this paper, we concentrate on
performing statistical analyses on the stress detection capability of two
popular approaches of training stress detection models with stress-related
biometric signals: user-dependent and user-independent models. Our research
manages to show that user-dependent models are statistically more accurate for
stress detection. In terms of effectiveness assessment, the balanced accuracy
(BA) metric is employed to evaluate the capability of distinguishing stress and
non-stress conditions of the models trained on either low-resolution or
high-resolution Electrodermal Activity (EDA) signals. The results from the
experiment show that training the model with (comparatively low-cost)
low-resolution EDA signal does not affect the stress detection accuracy of the
model significantly compared to using a high-resolution EDA signal. Our
research results demonstrate the potential of attaching the user-dependent
stress detection model trained on personal low-resolution EDA signal recorded
to collect data in daily life to provide users with personal stress level
insight and analysis.
