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

Aleksandra Badura, Aleksandra Masłowska, Andrzej Myśliwiec, Ewa Piętka

2021Sensors28 citationsDOIOpen Access PDF

Abstract

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.

Topics & Concepts

AdaBoostArtificial intelligencePattern recognition (psychology)Feature (linguistics)Computer sciencePhysical therapySupport vector machineWaveletWavelet transformPhysical medicine and rehabilitationSet (abstract data type)MedicineLinguisticsPhilosophyProgramming languageInfrared Thermography in MedicineAcupuncture Treatment Research StudiesMusculoskeletal pain and rehabilitation
Multimodal Signal Analysis for Pain Recognition in Physiotherapy Using Wavelet Scattering Transform | Litcius