Adaptive confidence learning for the personalization of pain intensity estimation systems

Kächele, Markus; Amirian, Mohammadreza; Thiam, Patrick; Werner, Philipp; Walter, Steffen; Palm, Günther; Schwenker, Friedhelm

doi:10.1007/s12530-016-9158-4

Cited by 64 publications

(44 citation statements)

References 31 publications

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“…ECG EMG EDA Fusion Werner et al [32] 62.00 57.90 73.80 74.10 Kächele et al [36,37] n. The performance metric consists of the average accuracy (in %) over the LOSO cross-validation. The best performing approach for each modality and the aggregation of all modalities is depicted in bold.…”

Section: Methodsmentioning

confidence: 99%

“…At a glance, 297 the designed deep learning architectures outperform the state-of-the-art results in every 298 setting, except for the ECG modality. Regarding the aggregation of all physiological 299 modalities, the second late fusion architecture performs best and sets a new 300 state-of-the-art fusion performance with an average accuracy of 83.76%, which even 301 outperforms the best fusion results reported in [37], where the authors could achieve an 302 average classification performance of 83.1% by using both physiological and video 303 features. The deep architecture based on the EDA modality significantly outperforms Table 3.…”

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confidence: 94%

“…The SLP was subsequently trained in a supervised 35 manner using backpropagation [15] to map the outputs of the CNN to the target 36 affective states. In [16], the authors proposed a multiple-fusion-layer based ensemble 37 classifier of stacked auto-encoder (MESAE) for emotion recognition based on 38 physiological data. A physiological-data-driven approach was proposed in order to 39 identify the structure of the ensemble.…”

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confidence: 99%

“…Several deep learning architectures are proposed for the 59 assessment of measurable physiological parameters in order to perform an end-to-end 60 classification of different levels of artificially induced nociceptive pain. The current work 61 aims at achieving state-of-the-art classification performances based on feature learning, 62 therefore removing the reliance on expert knowledge for the optimization of reliable personalized pain estimation in [37] based on both physiological and video modalities.…”

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confidence: 99%

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Exploring Deep Physiological Models for Nociceptive Pain Recognition

Thiam

Bellmann

Kestler³

et al. 2019

Preprint

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Standard feature engineering involves manually designing and assessing measurable descriptors based on some expert knowledge in the domain of application, followed by the selection of the best performing set of designed features in order to optimize an inference model. Several studies have shown that this whole manual process can be efficiently replaced by deep learning approaches which are characterized by the integration of feature engineering, feature selection and inference model optimization into a single learning process. Such techniques have proven to be very successful in the domain of image processing and have been able to attain state-of-the-art performances while significantly outperforming traditional approaches based on hand-crafted features. In the following work, we explore deep learning approaches for the analysis of physiological signals. More precisely, deep learning architectures are designed for the assessment of measurable physiological channels in order to perform an accurate classification of different levels of artificially induced nociceptive pain. Most of the previous works related to pain intensity classification based on physiological signals rely on a carefully designed set of hand-crafted features in order to achieve a relatively good classification performance. Therefore, the current work aims at building competitive pain intensity classification models without the need of domain specific expert knowledge for the generation of relevant features. The assessment of the designed deep learning architectures is based on the BioVid Heat Pain Database (Part A) and experimental validation demonstrates that 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 of 85.03% and 83.76% for the binary classification experiment consisting of the discrimination between the baseline level and the pain tolerance level (T 0 vs.T 4 ) in a Leave-One-Subject-Out (LOSO) cross-validation evaluation setting.

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Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 94%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Exploring Deep Physiological Models for Nociceptive Pain Recognition

Thiam

Bellmann

Kestler³

et al. 2019

Preprint

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“…During the experiments, the demeanour of each participant was recorded using several modalities consisting of video and bio-physiological channels for the BVDB, while the SEDB included audio, video and bio-physiological channels. The current work focuses uniquely on the video modality, and the reader should refer to the work in [10,[14][15][16]33,[59][60][61][62][63][64] for more experiments including the other recorded modalities.…”

Section: Datasets Descriptionmentioning

confidence: 99%

Two-Stream Attention Network for Pain Recognition from Video Sequences

Thiam

Kestler

Schwenker

2020

Sensors

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Several approaches have been proposed for the analysis of pain-related facial expressions. These approaches range from common classification architectures based on a set of carefully designed handcrafted features, to deep neural networks characterised by an autonomous extraction of relevant facial descriptors and simultaneous optimisation of a classification architecture. In the current work, an end-to-end approach based on attention networks for the analysis and recognition of pain-related facial expressions is proposed. The method combines both spatial and temporal aspects of facial expressions through a weighted aggregation of attention-based neural networks’ outputs, based on sequences of Motion History Images (MHIs) and Optical Flow Images (OFIs). Each input stream is fed into a specific attention network consisting of a Convolutional Neural Network (CNN) coupled to a Bidirectional Long Short-Term Memory (BiLSTM) Recurrent Neural Network (RNN). An attention mechanism generates a single weighted representation of each input stream (MHI sequence and OFI sequence), which is subsequently used to perform specific classification tasks. Simultaneously, a weighted aggregation of the classification scores specific to each input stream is performed to generate a final classification output. The assessment conducted on both the BioVid Heat Pain Database (Part A) and SenseEmotion Database points at the relevance of the proposed approach, as its classification performance is on par with state-of-the-art classification approaches proposed in the literature.

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