Multimodal Signal Acquisition for Pain Assessment in Physiotherapy

Badura, Aleksandra; Bieńkowska, Maria; Masłowska, Aleksandra; Czarlewski, Robert; Myśliwiec, Andrzej; Piętka, Ewa

doi:10.1007/978-3-030-49666-1_18

Cited by 4 publications

(2 citation statements)

References 20 publications

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“…The K-Force dynamometer was held in the right hand and it might be subconsciously squeezed, depending on the subjective pain intensity. The data were registered by the PainMonit platform, which delivers synchronized time-series with a pain label [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

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Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform

Badura

Masłowska²,

Myśliwiec³

et al. 2021

Sensors

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Fascial therapy is an effective, yet painful, procedure. Information about pain level is essential for the physiotherapist to adjust the therapy course and avoid potential tissue damage. We have developed a method for automatic pain-related reaction assessment in physiotherapy due to the subjectivity of a self-report. Based on a multimodal data set, we determine the feature vector, including wavelet scattering transforms coefficients. The AdaBoost classification model distinguishes three levels of reaction (no-pain, moderate pain, and severe pain). Because patients vary in pain reactions and pain resistance, our survey assumes a subject-dependent protocol. The results reflect an individual perception of pain in patients. They also show that multiclass evaluation outperforms the binary recognition.

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Section: Methodsmentioning

confidence: 99%

“…It is modeled by the Bateman function, and followed by the Laplace transform. The algorithm is described in more detail in [ 27 ].…”

Section: Methodsmentioning

confidence: 99%

Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform

Badura

Masłowska²,

Myśliwiec³

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

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Head Pose and Biomedical Signals Analysis in Pain Level Recognition

Bieńkowska,

Badura,

Myśliwiec

et al. 2022

Advances in Intelligent Systems and Computing

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An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition

Gouverneur,

Badura,

et al. 2024

Sci Data

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Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.

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Multimodal Signal Acquisition for Pain Assessment in Physiotherapy

Cited by 4 publications

References 20 publications

Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform

Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform

Head Pose and Biomedical Signals Analysis in Pain Level Recognition

An Experimental and Clinical Physiological Signal Dataset for Automated Pain Recognition

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