MIT Libraries logoDSpace@MIT

MIT
View Item 
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
  • DSpace@MIT Home
  • MIT Open Access Articles
  • MIT Open Access Articles
  • View Item
JavaScript is disabled for your browser. Some features of this site may not work without it.

Multi-task neural networks for personalized pain recognition from physiological signals

Author(s)
Lopez Martinez, Daniel; Picard, Rosalind W.
Thumbnail
DownloadSubmitted version (896.7Kb)
Terms of use
Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/
Metadata
Show full item record
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.
Date issued
2018-02
URI
https://hdl.handle.net/1721.1/121967
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
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
ISSN
978-1-5386-0680-3

Collections
  • MIT Open Access Articles

Browse

All of DSpaceCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

My Account

Login

Statistics

OA StatisticsStatistics by CountryStatistics by Department
MIT Libraries
PrivacyPermissionsAccessibilityContact us
MIT
Content created by the MIT Libraries, CC BY-NC unless otherwise noted. Notify us about copyright concerns.