Mobile Computing Framework for Machine Learning-Based Stress Detection

Stress is a pervasive issue in modern society, and a plethora of technological approaches have been developed for its detection. In this study, we propose an end-to-end framework for detecting chronic stress using only data collected from a smartwatch and a lightweight machine learning model that runs within a mobile application. Previous work achieved accurate results but required additional technology, such as cloud servers and custom sensors not readily available to the public. We tested lightweight models and found that a fine-tuned LightGBM achieved an $81.6 \% \mathrm{~F} 1$-score, while a hyperdimensional computing (HDC) model, optimizing efficiency with a slight accuracy tradeoff, reached 73%.