Personalized Prediction of Recurrent Stress Events Using Self-Supervised
  Learning on Multimodal Time-Series Data
Tanvir Islam Peter Washington
Chronic stress can significantly affect physical and mental health. The
advent of wearable technology allows for the tracking of physiological signals,
potentially leading to innovative stress prediction and intervention methods.
However, challenges such as label scarcity and data heterogeneity render stress
prediction difficult in practice. To counter these issues, we have developed a
multimodal personalized stress prediction system using wearable biosignal data.
We employ self-supervised learning (SSL) to pre-train the models on each
subject's data, allowing the models to learn the baseline dynamics of the
participant's biosignals prior to fine-tuning the stress prediction task. We
test our model on the Wearable Stress and Affect Detection (WESAD) dataset,
demonstrating that our SSL models outperform non-SSL models while utilizing
less than 5% of the annotations. These results suggest that our approach can
personalize stress prediction to each user with minimal annotations. This
paradigm has the potential to enable personalized prediction of a variety of
recurring health events using complex multimodal data streams.