Pseudo-labeling based adaptations of pain domain classifiers.

Introduction: 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.

Methods: 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.

Results and conclusions: 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.