# Personalized Driver Stress Detection with Multi-task Neural Networks using Physiological Signals

arXiv: https://arxiv.org/abs/1711.06116 · HTML: https://mlsanity.com/paper/1711.06116/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2017.

## Built on by (most cited first, 3 of 3)

- [Beyond One-Size-Fits-All: A Survey of Personalized Affective Computing in Human-Agent Interaction](https://mlsanity.com/paper/2304.00377/) (2023)
- [Learning Twofold Heterogeneous Multi-Task by Sharing Similar Convolution Kernel Pairs](https://mlsanity.com/paper/2101.12431/) (2021)
- [Improving Personalisation in Valence and Arousal Prediction using Data Augmentation](https://mlsanity.com/paper/2404.09042/) (2024)

## Abstract (arXiv)

Stress can be seen as a physiological response to everyday emotional, mental and physical challenges. A long-term exposure to stressful situations can have negative health consequences, such as increased risk of cardiovascular diseases and immune system disorder. Therefore, a timely stress detection can lead to systems for better management and prevention in future circumstances. In this paper, we suggest a multi-task learning based neural network approach (with hard parameter sharing of mutual representation and task-specific layers) for personalized stress recognition using skin conductance and heart rate from wearable devices. The proposed method is tested on multi-modal physiological responses collected during real-world and simulator driving tasks.