# Personalized Student Stress Prediction with Deep Multitask Network

arXiv: https://arxiv.org/abs/1906.11356 · HTML: https://mlsanity.com/paper/1906.11356/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jun 2019.

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

- [Analyzing the contribution of different passively collected data to predict Stress and Depression](https://mlsanity.com/paper/2310.13607/) (2023)
- [Learning Longitudinal Stress Dynamics from Irregular Self-Reports via Time Embeddings](https://mlsanity.com/paper/2509.01569/) (2025)

## Abstract (arXiv)

With the growing popularity of wearable devices, the ability to utilize physiological data collected from these devices to predict the wearer's mental state such as mood and stress suggests great clinical applications, yet such a task is extremely challenging. In this paper, we present a general platform for personalized predictive modeling of behavioural states like students' level of stress. Through the use of Auto-encoders and Multitask learning we extend the prediction of stress to both sequences of passive sensor data and high-level covariates. Our model outperforms the state-of-the-art in the prediction of stress level from mobile sensor data, obtaining a 45.6 % improvement in F1 score on the StudentLife dataset.