On-Device Transfer Learning for Personalising Psychological Stress Modelling using a Convolutional Neural Network

TL;DR

This paper presents an on-device transfer learning approach using a 1D CNN to personalize psychological stress detection based on physiological data, addressing individual variability and data collection challenges.

Contribution

It introduces a novel personalized, cross-domain 1D CNN model that leverages transfer learning from a base model trained on controlled data for real-world stress inference.

Findings

Improved stress detection accuracy with personalized models

Effective on-device transfer learning reduces data collection burden

Enhanced cross-domain generalization of stress inference

Abstract

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.