  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.
