Seamless monitoring of stress levels leveraging a foundational model for time sequences.

Abstract

Accurate and continuous monitoring of physiological stress is crucial, especially for patients with neurodegenerative diseases. Traditional monitoring methods, such as Electrocardiogram (ECG), are often invasive and limited in duration, while data from lightweight wearable devices, though more practical for seamless monitoring, typically suffers from significant quality degradation compared to clinical-grade measurements. The challenge lies in developing a robust, long-term, and patient-friendly stress monitoring system that overcomes the limitations of conventional approaches and the accuracy compromises of current wearables. Such a system must also provide actionable, interpretable insights for clinicians and adapt to individual patient variability. This manuscript introduces a methodology for seamless stress level monitoring by leveraging UniTS, a foundational model for time series. Our approach redefines stress detection as an anomaly detection problem, establishing a personalized baseline for each patient's physiological behavior. Furthermore, to enhance clinical utility and trust, the system integrates a Large Language Model (LLM) to generate human-readable explanations for detected anomalies. The proposed UniTS-based methodology demonstrates superior performance, outperforming 12 top-performing methods on three benchmark datasets. Crucially, it achieves performance comparable to that obtained from more invasive, clinical-grade devices (like ECG) even when utilizing data from lightweight wearable devices, thereby enabling truly seamless monitoring. Furthermore, the system has been successfully tested in a real-world environment, in the context of a project to monitor elderly patients with cognitive disorders in their homes. This work presents an advancement in physiological stress monitoring by offering a personalized, explainable, and continuously adaptive system. We extend and fine-tune UniTS to support contextual anomaly detection and LLM-driven explainability, addressing critical gaps in current healthcare monitoring, fostering enhanced clinician control, improved system predictability, and facilitating long-term, real-world applicability for patients with neurodegenerative conditions.