Subject-independent Pain Recognition using Physiological Signals and Para-linguistic Vocalizations

Nadeen Shoukry, Omar Elkilany, Patrick Thiam, Viktor Kessler, Friedhelm Schwenker

Abstract

Pain is the result of a complex interaction among the various parts of the human nervous system. It plays an important role in the diagnosis and treatment of patients. The standard method for pain recognition is self-report; however, not all patients can communicate pain effectively. In this work, the task of automated pain recognition is addressed using para-linguistic and physiological data. Hand-crafted and automatically generated features are extracted and evaluated independently. Several state-of-the-art machine learning algorithms are applied to perform subject-independent binary classification. The SenseEmotion dataset is used for evaluation and comparison. Random forests trained on hand-crafted features from the physiological modalities achieved an accuracy of 82.61%, while support vector machines trained on hand-crafted features from the para-linguistic data achieved an accuracy of 63.86%. Hand-crafted features outperformed automatically generated features.

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Paper Citation


in Harvard Style

Shoukry N., Elkilany O., Thiam P., Kessler V. and Schwenker F. (2020). Subject-independent Pain Recognition using Physiological Signals and Para-linguistic Vocalizations.In Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-397-1, pages 142-150. DOI: 10.5220/0008912201420150


in Bibtex Style

@conference{icpram20,
author={Nadeen Shoukry and Omar Elkilany and Patrick Thiam and Viktor Kessler and Friedhelm Schwenker},
title={Subject-independent Pain Recognition using Physiological Signals and Para-linguistic Vocalizations},
booktitle={Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2020},
pages={142-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008912201420150},
isbn={978-989-758-397-1},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Subject-independent Pain Recognition using Physiological Signals and Para-linguistic Vocalizations
SN - 978-989-758-397-1
AU - Shoukry N.
AU - Elkilany O.
AU - Thiam P.
AU - Kessler V.
AU - Schwenker F.
PY - 2020
SP - 142
EP - 150
DO - 10.5220/0008912201420150