S. Gruss et al.: Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines. In: PLoS One. Vol. 10, No. 10, 2015, S. 1–14, doi:10.1371/journal.pone.0140330.
S. Gruss et al.: Pain Intensity Recognition Rates via Biopotential Feature Patterns with Support Vector Machines. In: PLoS One. Vol. 10, No. 10, 2015, S. 1–14, doi:10.1371/journal.pone.0140330.
S. Walter et al.: Automatic pain quantification using autonomic parameters. In: Psychol. Neurosci. Nol. 7, No. 3, 2014, S. 363–380, doi:10.3922/j.psns.2014.041.
D. Lopez-Martinez, O. Rudovic, R. Picard: Physiological and behavioral profiling for nociceptive pain estimation using personalized multitask learning. November 2017, doi:10.1109/ACIIW.2017.8272611.
P. Werner, A. Al-Hamadi, K. Limbrecht-Ecklundt, S. Walter, S. Gruss, H. C. Traue: Automatic Pain Assessment with Facial Activity Descriptors. In: IEEE Trans. Affect. Comput. Vol. 8, No. 3, 2017, doi:10.1109/TAFFC.2016.2537327.
S. Brahnam, C. F. Chuang, F. Y. Shih, M. R. Slack: SVM classification of neonatal facial images of pain. In: Fuzzy Log. Appl. Vol. 3849, 2006, S. 121–128, doi:10.1007/11676935_15.
R. Niese et al.: Towards Pain Recognition in Post-Operative Phases Using 3D-based Features From Video and Support Vector Machines. In: JDCTA 3.4, 2009, S. 21–33, doi:10.4156/jdcta.vol3.issue4.2.
Philipp Werner, Ayoub Al-Hamadi, Kerstin Limbrecht-Ecklundt, Steffen Walter, Harald C. Traue: Head movements and postures as pain behavior. In: PLOS ONE. Band13, Nr.2, 14. Februar 2018, ISSN1932-6203, S.e0192767, doi:10.1371/journal.pone.0192767.
Patrick Thiam et al.: Multi-modal pain intensity recognition based on the senseemotion database. In: IEEE Transactions on Affective Computing, 2019, doi:10.1109/TAFFC.2019.2892090
S. Walter et al.: Data fusion for automated pain recognition. In: IEEE. 9th International Conference on Pervasive Computing Technologies for Healthcare. 2015, S. 261–264, doi:10.4108/icst.pervasivehealth.2015.259166.
Philipp Werner, Daniel Lopez-Martinez, Steffen Walter, Ayoub Al-Hamadi, Sascha Gruss: Automatic Recognition Methods Supporting Pain Assessment: A Survey. In: IEEE Transactions on Affective Computing. 2019, ISSN1949-3045, S.1–1, doi:10.1109/TAFFC.2019.2946774.
P. Lucey, J. F. Cohn, K. M. Prkachin, P. E. Solomon, I. Matthews: Painful data: The UNBC-McMaster shoulder pain expression archive database. In: IEEE Int. Conf. Autom. Face Gesture Recognit. Work. FG. 2011, S. 57–64, doi:10.1109/FG.2011.5771462.
S. Walter et al.: The biovid heat pain database: Data for the advancement and systematic validation of an automated pain recognition. In: IEEE International Conference on Cybernetics. CYBCONF 2013, doi:10.1109/CYBConf.2013.6617456.
M. S. H. Aung et al.: The Automatic Detection of Chronic Pain-Related Expression: Requirements, Challenges and the Multimodal EmoPain Dataset. In: IEEE Trans. Affect. Comput. Vol. 7, No. 4, 2016, S. 435–451, doi:10.1109/TAFFC.2015.2462830.
zdb-katalog.de
Philipp Werner, Ayoub Al-Hamadi, Kerstin Limbrecht-Ecklundt, Steffen Walter, Harald C. Traue: Head movements and postures as pain behavior. In: PLOS ONE. Band13, Nr.2, 14. Februar 2018, ISSN1932-6203, S.e0192767, doi:10.1371/journal.pone.0192767.
Philipp Werner, Daniel Lopez-Martinez, Steffen Walter, Ayoub Al-Hamadi, Sascha Gruss: Automatic Recognition Methods Supporting Pain Assessment: A Survey. In: IEEE Transactions on Affective Computing. 2019, ISSN1949-3045, S.1–1, doi:10.1109/TAFFC.2019.2946774.
