Multi-task neural networks for personalized pain recognition from physiological signals
Author(s)Lopez Martinez, Daniel; Picard, Rosalind W.
MetadataShow full item record
Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain.
DepartmentMassachusetts Institute of Technology. Institute for Medical Engineering & Science; Harvard University--MIT Division of Health Sciences and Technology; Massachusetts Institute of Technology. Media Laboratory
Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
Institute of Electrical and Electronics Engineers (IEEE)
Lopez-Martinez, Daniel and Rosalind Picard. "Multi-task neural networks for personalized pain recognition from physiological signals." Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW), October 2017, San Antonio, Texas, USA, IEEE, Febraury 2018 © 2017 IEEE