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

Aleksandra Badura1, Aleksandra Masłowska2, Andrzej Myśliwiec2

  • 1Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.

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Summary
This summary is machine-generated.

This study introduces an automated method to assess pain during fascial therapy, moving beyond subjective patient reports. The system accurately identifies pain levels, enabling personalized treatment adjustments and preventing tissue damage.

Keywords:
pain assessmentpain monitoringphysiotherapy

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Area of Science:

  • Physiotherapy
  • Biomedical Engineering
  • Pain Management

Background:

  • Fascial therapy is effective but painful, necessitating accurate pain monitoring.
  • Subjective pain self-reports can be unreliable, impacting treatment adjustments and patient safety.
  • Objective assessment of pain reactions is crucial for optimizing physiotherapy interventions.

Purpose of the Study:

  • To develop an automated method for assessing pain-related reactions during physiotherapy.
  • To improve the objectivity and reliability of pain assessment in clinical settings.
  • To enable personalized adjustments to fascial therapy based on real-time pain feedback.

Main Methods:

  • Utilized a multimodal dataset to extract a feature vector, incorporating wavelet scattering transform coefficients.
  • Developed an AdaBoost classification model to distinguish between three pain levels: no pain, moderate pain, and severe pain.
  • Implemented a subject-dependent protocol to account for individual variations in pain perception and resistance.

Main Results:

  • The developed method accurately assesses pain-related reactions in physiotherapy.
  • The AdaBoost model successfully classified three distinct levels of patient pain.
  • Subject-dependent analysis revealed individual pain perception, highlighting the limitations of generalized pain assessment.
  • Multiclass evaluation demonstrated superior performance compared to binary pain recognition.

Conclusions:

  • Automated pain assessment in physiotherapy is feasible and beneficial for patient care.
  • The subject-dependent approach provides a more accurate reflection of individual pain experiences.
  • Multiclass pain level recognition offers a more nuanced understanding than simple pain/no-pain distinctions.
  • This technology can aid clinicians in adjusting therapy to enhance effectiveness and safety.