An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals

TL;DR

This paper proposes an adaptive, personalized stress detection system for wearable devices using physiological signals, aiming to improve accuracy over traditional generalized models by tailoring to individual users.

Contribution

It introduces a novel three-stage adaptive framework for personalized stress detection that adapts models to individual physiological data, enhancing accuracy and applicability.

Findings

Framework improves stress detection accuracy for individuals

Personalized models outperform generalized models

Potential for broader mental health applications

Abstract

Timely stress detection is crucial for protecting vulnerable groups from long-term detrimental effects by enabling early intervention. Wearable devices, by collecting real-time physiological signals, offer a solution for accurate stress detection accommodating individual differences. This position paper introduces an adaptive framework for personalized stress detection using PPG and EDA signals. Unlike traditional methods that rely on a generalized model, which may suffer performance drops when applied to new users due to domain shifts, this framework aims to provide each user with a personalized model for higher stress detection accuracy. The framework involves three stages: developing a generalized model offline with an initial dataset, adapting the model to the user's unlabeled data, and fine-tuning it with a small set of labeled data obtained through user interaction. This approach not only offers a foundation for mobile applications that provide personalized stress detection and intervention but also has the potential to address a wider range of mental health issues beyond stress detection using physiological signals.