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Authors: Nadeen Shoukry ; Omar Elkilany ; Patrick Thiam ; Viktor Kessler and Friedhelm Schwenker

Affiliation: Ulm University, Institute of Neural Information Processing, 89081 Ulm, Germany

Keyword(s): Machine Learning, Pain Recognition, Affective Computing, Biopotential Signals, Para-linguistic Data.

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 several formats:
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 - ICPRAM; ISBN 978-989-758-397-1; ISSN 2184-4313, SciTePress, pages 142-150. DOI: 10.5220/0008912201420150

@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 - ICPRAM},
year={2020},
pages={142-150},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008912201420150},
isbn={978-989-758-397-1},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 9th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Subject-independent Pain Recognition using Physiological Signals and Para-linguistic Vocalizations
SN - 978-989-758-397-1
IS - 2184-4313
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
PB - SciTePress

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