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
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TLDR
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.Abstract:
The subjective nature of pain makes it a very challenging phenomenon to assess. Most of the current pain assessment approaches rely on an individual’s ability to recognise and report an observed pain episode. However, pain perception and expression are affected by numerous factors ranging from personality traits to physical and psychological health state. Hence, several approaches have been proposed for the automatic recognition of pain intensity, based on measurable physiological and audiovisual parameters. In the current paper, an assessment of several fusion architectures for the development of a multi-modal pain intensity classification system is performed. The contribution of the presented work is two-fold: (1) 3 distinctive modalities consisting of audio, video and physiological channels are assessed and combined for the classification of several levels of pain elicitation. (2) 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. The assessment is based on the SenseEmotion Database and experimental validation demonstrates the relevance of the multi-modal classification approach, which achieves classification rates of respectively $83.39\%$ 83 . 39 % , $59.53\%$ 59 . 53 % and $43.89\%$ 43 . 89 % in a 2-class, 3-class and 4-class pain intensity classification task.read more
Citations
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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.
Journal ArticleDOI
Two-Stream Attention Network for Pain Recognition from Video Sequences.
TL;DR: An end-to-end approach based on attention networks for the analysis and recognition of pain-related facial expressions is proposed, which combines both spatial and temporal aspects of facial expressions through a weighted aggregation of attention-based neural networks’ outputs.
Proceedings ArticleDOI
Multimodal Deep Denoising Convolutional Autoencoders for Pain Intensity Classification based on Physiological Signals
TL;DR: The introduction of trainable weighting parameters for the generation of an aggregated latent representation outperforms most of the previously proposed methods in related works, each based on a set of carefully selected hand-crafted features.
Journal ArticleDOI
Computer aided pain detection and intensity estimation using compact CNN based fusion network
Ashish Semwal,Narendra D. Londhe +1 more
TL;DR: Joint learning of robust pain-related facial expression features from fused RGB appearance and shape-based latent representation results in a more robust pain assessment model as compared to learning from either of the representation independently.
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