An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals
<|reference_start|>An Adaptive System for Wearable Devices to Detect Stress Using Physiological Signals: 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.<|reference_end|>
@article{xu2024an,
    title={An Adaptive System for Wearable Devices to Detect Stress Using
  Physiological Signals},
    author={Gelei Xu, Ruiyang Qin, Zhi Zheng, Yiyu Shi},
    journal={arXiv preprint arXiv:2407.15252},
    year={2024},
    archivePrefix={arXiv},
    eprint={2407.15252},
    primaryClass={cs.CV}
}