# Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data

arXiv: https://arxiv.org/abs/2307.03337 · HTML: https://mlsanity.com/paper/2307.03337/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 7 Jul 2023.

## Builds on (most cited first, 10 of 10)

- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification](https://mlsanity.com/paper/1502.01852/) (2015)
- [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://mlsanity.com/paper/1512.02595/) (2015)
- [Subject-Aware Contrastive Learning for Biosignals](https://mlsanity.com/paper/2007.04871/) (2020)
- [Rethinking the Augmentation Module in Contrastive Learning: Learning Hierarchical Augmentation Invariance with Expanded Views](https://mlsanity.com/paper/2206.00227/) (2022)
- [A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism](https://mlsanity.com/paper/2303.03577/) (2023)
- [MuSE-ing on the Impact of Utterance Ordering On Crowdsourced Emotion Annotations](https://mlsanity.com/paper/1903.11672/) (2019)
- [Employing Multimodal Machine Learning for Stress Detection](https://mlsanity.com/paper/2306.09385/) (2023)
- [Challenges and Opportunities for Machine Learning Classification of Behavior and Mental State from Images](https://mlsanity.com/paper/2201.11197/) (2022)
- [A Perspective on Crowdsourcing and Human-in-the-Loop Workflows in Precision Health](https://mlsanity.com/paper/2303.03578/) (2023)

## Built on by (most cited first, 2 of 2)

- [A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study](https://mlsanity.com/paper/2308.14245/) (2023)
- [Multimodal Stress Detection Using Facial Landmarks and Biometric Signals](https://mlsanity.com/paper/2311.03606/) (2023)

## Abstract (arXiv)

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.