
95% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
ORIGINAL RESEARCH article
Front. Pain Res.
Sec. Pain Research Methods
Volume 6 - 2025 | doi: 10.3389/fpain.2025.1562099
This article is part of the Research Topic Integrating Sensors and Artificial Intelligence for Objective Pain Detection and Quantification: Unveiling New Possibilities View all 4 articles
The final, formatted version of the article will be published soon.
You have multiple emails registered with Frontiers:
Please enter your email address:
If you already have an account, please login
You don't have a Frontiers account ? You can register here
Each human being experiences pain differently. In addition to the highly subjective phenomenon, only limited labeled data, mostly based on short-term pain sequences recorded in a lab setting, is available. However, human beings in a clinic might suffer from long painful time periods for which even a smaller amount of data, in comparison to the short-term pain sequences, is available.The characteristics of short-term and long-term pain sequences are different with respect to the reactions of the human body. However, for an accurate pain assessment, representative data is necessary. Although pain recognition techniques, reported in the literature, perform well on short-term pain sequences. The collection of labeled long-term pain sequences is challenging and techniques for the assessment of long-term pain episodes are still rare. To create accurate pain assessment systems for the long-term pain domain a knowledge transfer from the short-term pain domain is inevitable. In this study, we adapt classifiers for the short-term pain domain to the long-term pain domain using pseudo-labeling techniques. We analyze the short-term and long-term pain recordings of physiological signals in combination with electric and thermal pain stimulation. The results of the study show that it is beneficial to augment the training set with the pseudo labeled long-term domain samples. For the electric pain domain in combination with the early fusion approach, we improved the classification performance by 2.4% to 80.4% in comparison to the basic approach. For the thermal pain domain in combination with the early fusion approach, we improved the classification performance by 2.8% to 70.0% in comparison to the basic approach.
Keywords: Domain adaptation, e-health, pain duration transfer, Pain recognition, physiological signals, Pseudo-labeling, Signal segmentation
Received: 16 Jan 2025; Accepted: 28 Mar 2025.
Copyright: © 2025 Ricken, Gruss, Walter and Schwenker. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Tobias Benjamin Ricken, Ulm University, Ulm, Germany
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.
Research integrity at Frontiers
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.