Automatic Coding of Facial Expressions of Pain: Are We There Yet?

Lautenbacher S, Hassan T, Seuss D, Loy FW, Garbas J-U, Schmid U, Kunz M (2022)
Pain Research & Management 2022: 6635496.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
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Autor*in
Lautenbacher, Stefan; Hassan, TeenaUniBi; Seuss, Dominik; Loy, Frederik W; Garbas, Jens-Uwe; Schmid, Ute; Kunz, Miriam
Abstract / Bemerkung
Introduction: The experience of pain is regularly accompanied by facial expressions. The gold standard for analyzing these facial expressions is the Facial Action Coding System (FACS), which provides so-called action units (AUs) as parametrical indicators of facial muscular activity. Particular combinations of AUs have appeared to be pain-indicative. The manual coding of AUs is, however, too time- and labor-intensive in clinical practice. New developments in automatic facial expression analysis have promised to enable automatic detection of AUs, which might be used for pain detection.; Objective: Our aim is to compare manual with automatic AU coding of facial expressions of pain.; Methods: FaceReader7 was used for automatic AU detection. We compared the performance of FaceReader7 using videos of 40 participants (20 younger with a mean age of 25.7 years and 20 older with a mean age of 52.1 years) undergoing experimentally induced heat pain to manually coded AUs as gold standard labeling. Percentages of correctly and falsely classified AUs were calculated, and we computed as indicators of congruency, "sensitivity/recall," "precision," and "overall agreement (F1)."; Results: The automatic coding of AUs only showed poor to moderate outcomes regarding sensitivity/recall, precision, and F1. The congruency was better for younger compared to older faces and was better for pain-indicative AUs compared to other AUs.; Conclusion: At the moment, automatic analyses of genuine facial expressions of pain may qualify at best as semiautomatic systems, which require further validation by human observers before they can be used to validly assess facial expressions of pain. Copyright © 2022 Stefan Lautenbacher et al.
Erscheinungsjahr
2022
Zeitschriftentitel
Pain Research & Management
Band
2022
Art.-Nr.
6635496
ISSN
1203-6765
Page URI
https://pub.uni-bielefeld.de/record/2960853

Zitieren

Lautenbacher S, Hassan T, Seuss D, et al. Automatic Coding of Facial Expressions of Pain: Are We There Yet? Pain Research & Management. 2022;2022: 6635496.
Lautenbacher, S., Hassan, T., Seuss, D., Loy, F. W., Garbas, J. - U., Schmid, U., & Kunz, M. (2022). Automatic Coding of Facial Expressions of Pain: Are We There Yet? Pain Research & Management, 2022, 6635496. https://doi.org/10.1155/2022/6635496
Lautenbacher, S., Hassan, T., Seuss, D., Loy, F. W., Garbas, J. - U., Schmid, U., and Kunz, M. (2022). Automatic Coding of Facial Expressions of Pain: Are We There Yet? Pain Research & Management 2022: 6635496.
Lautenbacher, S., et al., 2022. Automatic Coding of Facial Expressions of Pain: Are We There Yet? Pain Research & Management, 2022: 6635496.
S. Lautenbacher, et al., “Automatic Coding of Facial Expressions of Pain: Are We There Yet?”, Pain Research & Management, vol. 2022, 2022, : 6635496.
Lautenbacher, S., Hassan, T., Seuss, D., Loy, F.W., Garbas, J.-U., Schmid, U., Kunz, M.: Automatic Coding of Facial Expressions of Pain: Are We There Yet? Pain Research & Management. 2022, : 6635496 (2022).
Lautenbacher, Stefan, Hassan, Teena, Seuss, Dominik, Loy, Frederik W, Garbas, Jens-Uwe, Schmid, Ute, and Kunz, Miriam. “Automatic Coding of Facial Expressions of Pain: Are We There Yet?”. Pain Research & Management 2022 (2022): 6635496.

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