  Pain is a complex phenomenon which is manifested and expressed by patients in
various forms. The immediate and objective recognition of it is a great of
importance in order to attain a reliable and unbiased healthcare system. In
this work, we elaborate electrocardiography signals revealing the existence of
variations in pain perception among different demographic groups. We exploit
this insight by introducing a novel multi-task neural network for automatic
pain estimation utilizing the age and the gender information of each
individual, and show its advantages compared to other approaches.
