Multi-task neural networks for personalized pain recognition from physiological signals
Name
1708.08755.pdf
Description
Submitted version
Size
896.78 KB
Format
Adobe PDF
Checksum (MD5)
dfa5871436d893ff5731858da10d0efa
Author(s)
Lopez Martinez, Daniel
Picard, Rosalind W.
Date Issued
February 2018
Journal
Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
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
Version
Original manuscript
Abstract
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.
MIT Department
Massachusetts Institute of Technology. Institute for Medical Engineering & Science
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Media Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/ACIIW.2017.8272611
