An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals
This is an incremental approach for vulnerable groups needing timely stress detection to enable early intervention.
The paper tackles the problem of stress detection using wearable devices by proposing an adaptive framework for personalized models based on PPG and EDA signals, aiming to improve accuracy over generalized models that suffer from domain shifts.
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.