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

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 subjectivit...

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Bibliographic Details
Published inSensors (Basel, Switzerland) Vol. 21; no. 4; p. 1311
Main Authors Badura, Aleksandra, Masłowska, Aleksandra, Myśliwiec, Andrzej, Piętka, Ewa
Format Journal Article
LanguageEnglish
Published Switzerland MDPI 12.02.2021
MDPI AG
Subjects
Humans
Pain
pain assessment
Pain Measurement
pain monitoring
Physical Therapy Modalities
physiotherapy
Wavelet Analysis
pain assessment
physiotherapy
pain monitoring
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Summary: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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ISSN:1424-8220
1424-8220
DOI:10.3390/s21041311