Edge Device Based Stress Detection For Older Adults With Cortisol Biomarker

Stress impairs human cognition and over long periods of time can cause severe negative health effects for older adults. Real-time stress detection can be a useful tool in stress mitigation strategies, but stress detection is often implemented with machine learning models too large to run on an edge device. This paper presents a stress detection framework for older adults which is designed to be executed with low latency by a wearable device or other embedded system. We use Galvanic Skin Response, Blood Volume Pulse, Interbeat Interval, and skin temperature data with cortisol as a stress biomarker to evaluate the performance of several machine learning models. The best performing model was a radial basis function-based SVC (Support Vector Machine). The model achieved a test data accuracy of 82% and a test data F1-Score of 0.70. Performance data indicates that the framework performs well and could be improved for use on a wearable device.