Bio-Visual Fusion for Person-Independent Recognition of Pain Intensity

Kächele, Markus; Werner, Philipp; Al-Hamadi, Ayoub; Palm, Günther; Walter, Steffen; Schwenker, Friedhelm

doi:10.1007/978-3-319-20248-8_19

Cited by 53 publications

(47 citation statements)

References 18 publications

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“…The next best thing however is a classifier that has been trained on as much data as possible, in this case the 86 persons in the training set. This approach is inspired and supported by the relatively high classification accuracies of such classifiers in earlier studies on the same dataset [9], [10]. Concretely, a Random Forest classifier is trained on each available person (besides the test person) and it is then used to assign proxy labels to the data of the test person.…”

Section: ) Machine Learning Based Measuresmentioning

confidence: 99%

“…pain vs. no pain) is exchanged for a continuous domain with different pain intensity levels and is posed as a regression problem. To this date, research in automatic pain estimation has mainly focused on fixed classes and besides [2] and [9] we are not aware of systems for which the fixed classed are exchanged for a fully continuous pain intensity estimation. We furthermore show that the system is able to recognize pain in real-time from a subset of the presented features.…”

Section: Introductionmentioning

confidence: 99%

“…heart rate, electrodermal activity) [8], linguistic and paralinguistic measures or video signals, into complex classification systems. Pain recognition based on multi-modal signals has recently been proven to be very effective, most often outperforming unimodal systems significantly [9], [10].…”

Section: Introductionmentioning

confidence: 99%

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Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

Kächele

Thiam

Amirian

et al. 2016

IEEE J. Sel. Top. Signal Process.

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In this work, we present methods for the personalization of a system for the continuous estimation of pain intensity from bio-physiological channels. We investigate various ways to estimate the similarity of persons and to retrieve the most informative ones using meta information, personality traits and machine learning techniques. Given this information, specialized classifiers can be created that are both, more efficient in terms of complexity and training times and also more accurate than classifiers trained on the complete data. To capture the most information in the different bio-physiological channels, we cover a broad spectrum of different feature extraction algorithms. Furthermore, we show that the system is capable of running in real-time and discuss issues that arise when dealing with incremental data processing. In extensive experiments we verify the validity of our approach.

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Section: ) Machine Learning Based Measuresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

Kächele

Thiam

Amirian

et al. 2016

IEEE J. Sel. Top. Signal Process.

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“…Gruss et al [8] also applied SVMs to the same dataset, but only used EMG, ECG and SC features. These features have also been used in combination with behavioral features derived from video [24], [25], [26]. Recently, Kchele et al [5] proposed a method for personalized prediction of pain intensity based on similarity measures, using EMG, ECG and SC features, together with meta-information.…”

Section: Related Workmentioning

confidence: 99%

Multi-task neural networks for personalized pain recognition from physiological signals

Lopez-Martinez

Picard

2017

2017 Seventh International Conference on Affective Computing and Intelligent Interaction Workshops and Demos (ACIIW)

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Pain is a complex and subjective experience that poses a number of measurement challenges. While self-report by the patient is viewed as the gold standard of pain assessment, this approach fails when patients cannot verbally communicate pain intensity or lack normal mental abilities. Here, we present a pain intensity measurement method based on physiological signals. Specifically, we implement a multi-task learning approach based on neural networks that accounts for individual differences in pain responses while still leveraging data from across the population. We test our method in a dataset containing multi-modal physiological responses to nociceptive pain.

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“…The objective assessment of chronic pain is a difficult problem that can be of great interest in multiple clinical areas to procure a better patient intervention and an improvement in the quality of life of chronic patients of different etiology. It has been conducted in the bibliography from different points of view, ranging from video signal processing [29,31,32] or the analysis of bio-physiological signals [33][34][35][36], to systems combining heterogeneous information sources [3,11,27,[37][38][39][40]. However, to date a universal method has been developed neither for the objective evaluation of pain [22], nor for the measurement of its seriousness, due to the subjective nature of pain perception by the sufferer, although it is a critical demand for multiple disorders.…”

Section: Introductionmentioning

confidence: 99%

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

Naranjo-Hernández

Reina‐Tosina

Roa

2020

Sensors

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Non-oncologic chronic pain is a common high-morbidity impairment worldwide and acknowledged as a condition with significant incidence on quality of life. Pain intensity is largely perceived as a subjective experience, what makes challenging its objective measurement. However, the physiological traces of pain make possible its correlation with vital signs, such as heart rate variability, skin conductance, electromyogram, etc., or health performance metrics derived from daily activity monitoring or facial expressions, which can be acquired with diverse sensor technologies and multisensory approaches. As the assessment and management of pain are essential issues for a wide range of clinical disorders and treatments, this paper reviews different sensor-based approaches applied to the objective evaluation of non-oncological chronic pain. The space of available technologies and resources aimed at pain assessment represent a diversified set of alternatives that can be exploited to address the multidimensional nature of pain.

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Bio-Visual Fusion for Person-Independent Recognition of Pain Intensity

Cited by 53 publications

References 18 publications

Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

Methods for Person-Centered Continuous Pain Intensity Assessment From Bio-Physiological Channels

Multi-task neural networks for personalized pain recognition from physiological signals

Sensor Technologies to Manage the Physiological Traits of Chronic Pain: A Review

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