  Pain is a subjective experience commonly measured through patient's self
report. While there exist numerous situations in which automatic pain
estimation methods may be preferred, inter-subject variability in physiological
and behavioral pain responses has hindered the development of such methods. In
this work, we address this problem by introducing a novel personalized
multitask machine learning method for pain estimation based on individual
physiological and behavioral pain response profiles, and show its advantages in
a dataset containing multimodal responses to nociceptive heat pain.
