  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.
