Personalized Driver Stress Detection with Multi-task Neural Networks using Physiological Signals
This addresses stress management for drivers, but it is incremental as it applies an existing multi-task learning approach to a specific domain.
The paper tackles personalized driver stress detection by proposing a multi-task neural network with hard parameter sharing, using physiological signals like skin conductance and heart rate from wearable devices, and tests it on data from real-world and simulator driving tasks, but no concrete performance numbers are provided.
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.