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arXiv · 2407.15252

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

Gelei Xu·Ruiyang Qin·Zhi Zheng·Yiyu Shi
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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.

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Gelei Xu, Ruiyang Qin, Zhi Zheng, Yiyu Shi. 2024-07-21. An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals. https://arxiv.org/abs/2407.15252

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