  Monitoring the stress level in patients with neurodegenerative diseases can
help manage symptoms, improve patient's quality of life, and provide insight
into disease progression. In the literature, ECG, actigraphy, speech, voice,
and facial analysis have proven effective at detecting patients' emotions. On
the other hand, these tools are invasive and do not integrate smoothly into the
patient's daily life. HRV has also been proven to effectively indicate stress
conditions, especially in combination with other signals. However, when HRV is
derived from less invasive devices than the ECG, like smartwatches and
bracelets, the quality of measurements significantly degrades. This paper
presents a methodology for stress detection from a smartwatch based on a
universal model for time series, UniTS, which we fine-tuned for the task. We
cast the problem as anomaly detection rather than classification to favor model
adaptation to individual patients and allow the clinician to maintain greater
control over the system's predictions. We demonstrate that our proposed model
considerably surpasses 12 top-performing methods on 3 benchmark datasets.
Furthermore, unlike other state-of-the-art systems, UniTS enables seamless
monitoring, as it shows comparable performance when using signals from invasive
or lightweight devices.
