Analysing the Performance of Stress Detection Models on Consumer-Grade Wearable Devices

TL;DR

This study evaluates the effectiveness of stress detection models using low-resolution EDA signals from consumer wearables, finding that such models are nearly as accurate as high-resolution ones, especially when user-dependent.

Contribution

It demonstrates that low-resolution EDA signals from consumer devices can effectively be used for personalized stress detection models, reducing data collection costs.

Findings

User-dependent models are statistically more accurate.

Low-resolution EDA signals do not significantly reduce detection accuracy.

Personalized models can provide daily stress insights.

Abstract

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.