P
Peter Bellmann
Researcher at University of Ulm
Publications - 24
Citations - 161
Peter Bellmann is an academic researcher from University of Ulm. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 5, co-authored 18 publications receiving 97 citations.
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Journal ArticleDOI
Multi-Modal Pain Intensity Recognition Based on the SenseEmotion Database
Patrick Thiam,Viktor Kessler,Mohammadreza Amirian,Peter Bellmann,Georg Layher,Yan Zhang,Maria Velana,Sascha Gruss,Steffen Walter,Harald C. Traue,Daniel Schork,Jonghwa Kim,Elisabeth André,Heiko Neumann,Friedhelm Schwenker +14 more
TL;DR: Three distinctive modalities consisting of audio, video and physiological channels are assessed and combined for the classification of several levels of pain elicitation and an extensive assessment of several fusion strategies is carried out in order to design a classification architecture that improves the performance of the pain recognition system.
Journal ArticleDOI
Exploring Deep Physiological Models for Nociceptive Pain Recognition.
TL;DR: The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database and experimental validation demonstrates that the proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous methods reported in the literature.
Book ChapterDOI
Multi-classifier-Systems: Architectures, Algorithms and Applications
TL;DR: In this work multi-classifier-systems (MCS) are discussed, several fixed and trainable aggregation rules are presented and several criteria to measure diversity in MCS are defined.
Journal ArticleDOI
Ordinal Classification: Working Definition and Detection of Ordinal Structures
TL;DR: This study proposes a working definition for OC tasks, which is based on the decision boundaries of standard binary Support Vector Machines, and introduces a simple algorithm for the detection of ordinal structures.
Posted ContentDOI
Exploring Deep Physiological Models for Nociceptive Pain Recognition
TL;DR: The proposed uni-modal architecture for the electrodermal activity (EDA) and the deep fusion approaches significantly outperform previous classification methods reported in the literature, with respective average performances.