  Stress is a growing concern in modern society adversely impacting the wider
population more than ever before. The accurate inference of stress may result
in the possibility for personalised interventions. However, individual
differences between people limits the generalisability of machine learning
models to infer emotions as people's physiology when experiencing the same
emotions widely varies. In addition, it is time consuming and extremely
challenging to collect large datasets of individuals' emotions as it relies on
users labelling sensor data in real-time for extended periods. We propose the
development of a personalised, cross-domain 1D CNN by utilising transfer
learning from an initial base model trained using data from 20 participants
completing a controlled stressor experiment. By utilising physiological sensors
(HR, HRV EDA) embedded within edge computing interfaces that additionally
contain a labelling technique, it is possible to collect a small real-world
personal dataset that can be used for on-device transfer learning to improve
model personalisation and cross-domain performance.
