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Multi-modal pain intensity assessment based on physiological signals: a deep learning perspective

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peer-reviewed

Erstveröffentlichung
2021-09-01
Authors
Thiam, Patrick
Hihn, Heinke
Braun, Daniel A.
Kestler, Hans A.
Schwenker, Friedhelm
Wissenschaftlicher Artikel


Published in
Frontiers in Physiology ; 12 (2021). - Art.-Nr. 720464. - eISSN 1664-042X
Link to original publication
https://dx.doi.org/10.3389/fphys.2021.720464
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
Medizinische Fakultät
Institutions
Institut für Medizinische Systembiologie
Institut für Nachrichtentechnik
Document version
published version (publisher's PDF)
Abstract
Traditional pain assessment approaches ranging from self-reporting methods, to observational scales, rely on the ability of an individual to accurately assess and successfully report observed or experienced pain episodes. Automatic pain assessment tools are therefore more than desirable in cases where this specific ability is negatively affected by various psycho-physiological dispositions, as well as distinct physical traits such as in the case of professional athletes, who usually have a higher pain tolerance as regular individuals. Hence, several approaches have been proposed during the past decades for the implementation of an autonomous and effective pain assessment system. These approaches range from more conventional supervised and semi-supervised learning techniques applied on a set of carefully hand-designed feature representations, to deep neural networks applied on preprocessed signals. Some of the most prominent advantages of deep neural networks are the ability to automatically learn relevant features, as well as the inherent adaptability of trained deep neural networks to related inference tasks. Yet, some significant drawbacks such as requiring large amounts of data to train deep models and over-fitting remain. Both of these problems are especially relevant in pain intensity assessment, where labeled data is scarce and generalization is of utmost importance. In the following work we address these shortcomings by introducing several novel multi-modal deep learning approaches (characterized by specific supervised, as well as self-supervised learning techniques) for the assessment of pain intensity based on measurable bio-physiological data. While the proposed supervised deep learning approach is able to attain state-of-the-art inference performances, our self-supervised approach is able to significantly improve the data efficiency of the proposed architecture by automatically generating physiological data and simultaneously performing a fine-tuning of the architecture, which has been previously trained on a significantly smaller amount of data.
EU Project uulm
BRISC / Bounded Rationality in Sensorimotor Coordination / EC / H2020 / 678082
DFG Project THU
Multimodale Affekterkennung im Verlaufe eines tutoriellen Lernexperiments / DFG / 394413900 [SCHW623/7-1]
Project uulm
SenseEmotion / Verbundprojekt: Multisensorische Schmerz- & Emotionserkennung: Avatar-basiertes Affektmanagement für ältere Menschen - SenseEmotion - Teilvorhaben: Affektive Interaktion: Audiovisuelle Analyse und avatarbasierte Interaktion / BMBF / 16SV7310
coNfirm - Netzwerke der Herzerkrankungen. Systemmedizinischer Ansatz zur Verbesserung der Herzgesundheit - Multi-spezies und multi-level Daten Ressource (TP1) und Bioinformatische Ansätze zur Generierung von gemeinsamen Signalwegen kardialer Erkrankungen (TP2) / BMBF / 01ZX1708C
ZIV / Zentrum für Innovative Versorgung / MWK Baden-Württemberg
Subject headings
[GND]: Algesimetrie | Schmerz | Signalverarbeitung
[LCSH]: Signal processing
[MeSH]: Pain measurement
[Free subject headings]: physiological signals | deep neural networks | information fusion | pain intensity assessment
[DDC subject group]: DDC 150 / Psychology | DDC 620 / Engineering & allied operations
License
CC BY 4.0 International
https://creativecommons.org/licenses/by/4.0/

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DOI & citation

Please use this identifier to cite or link to this item: http://dx.doi.org/10.18725/OPARU-43668

Thiam, Patrick et al. (2022): Multi-modal pain intensity assessment based on physiological signals: a deep learning perspective. Open Access Repositorium der Universität Ulm und Technischen Hochschule Ulm. http://dx.doi.org/10.18725/OPARU-43668
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